Together AI

Role Description Together AI is building the AI Acceleration Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art AI cloud infrastructure. The AI Infrastructure team at Together AI is at the forefront of building and scaling the foundational systems that power our generative AI platform. The storage and observability team is crucial for designing, implementing, and maintaining robust distributed storage solutions, ensuring seamless data access and management. They are also responsible for developing comprehensive observability platforms, providing critical insights into system performance and GPU utilization, and proactively identifying and resolving issues. - Design and implement a scalable observability platform (metrics, logs, traces) using tools like Prometheus, Grafana, ClickHouse, ClickStack, and OpenTelemetry, including telemetry data pipelines and log aggregation workflows. - Develop automated monitoring, alerting, and anomaly detection systems, including SLIs/SLOs, runbooks, and predictive analytics for critical services. - Build and deploy custom observability tools and infrastructure-as-code using Go, Python, Terraform, Ansible, and Helm. - Collaborate with engineering teams to enhance distributed tracing and application monitoring, and lead incident response with post-mortem analysis. - Define observability best practices. Qualifications - Expertise in observability platforms (Prometheus, Grafana, ClickStack, OpenTelemetry) and cloud-native monitoring services (AWS, GCP, Azure). - Strong programming skills in Go, Python, or similar languages, with proficiency in infrastructure-as-code tools (Terraform, Ansible, Helm). - Experience designing, operating, and scaling large-scale distributed systems and pipelines for high-volume data ingestion and real-time querying. - Deep understanding of containerization (Docker) and orchestration (Kubernetes). - Knowledge of microservices architecture, service mesh technologies, CI/CD pipelines, and GitOps workflows. - Expertise in managing databases (PostgreSQL, MongoDB, Redis) and time-series databases with high-cardinality data. Requirements - Experience monitoring AI/ML infrastructure, GPU clusters, and custom metrics for model performance and training pipelines. - Background in high-frequency, low-latency systems monitoring, chaos engineering, and reliability testing. - Contributions to open-source observability projects. - Familiarity with security monitoring and compliance frameworks. Benefits - Competitive compensation. - Startup equity. - Health insurance. - Flexibility in terms of remote work. - The US base salary range for this full-time position is: $200,000 - $280,000 + equity + benefits. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Role Description The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. As a Platform Engineer in Model Shaping, you will work at the intersection of backend engineering and infrastructure, building the foundational layers of Together’s platform for model customization and evaluation. You will design, develop, and operate both the backend services and the underlying systems that enable us to sustainably and reliably scale production workflows launched by our users, as well as internal research experiments. You will operate in a cross-functional environment, collaborating with other engineers and researchers in the team to improve the infrastructure based on the needs of projects they work on. You will also interact with other engineering teams at Together (such as Commerce, Data Engineering, and Cloud Infrastructure) to integrate the services developed by Model Shaping with systems developed by those teams. Responsibilities - Design and build Together’s systems and infrastructure for model customization, including user-facing features and internal improvements - Contribute to reliability improvements for the platform, participating in an on-call rotation and improving processes for incident response - Create and improve internal tooling for deployment, continuous integration, and observability - Build a job orchestration platform spanning multiple datacenters, supporting a highly heterogeneous hardware landscape - Partner with teams developing internal services, co-designing these services and incorporating them in systems built within Together Qualifications - 3+ years of experience in building infrastructure or backend components of production services - Extensive experience designing, operating, and troubleshooting production Linux environments and Kubernetes-based platforms - Strong software engineering background in Python or Go - Experienced with infrastructure automation tools (Terraform, Ansible), monitoring/observability stacks (Prometheus, Grafana), and CI/CD pipelines (GitHub Actions, ArgoCD) - Cloud environment (e.g., AWS/GCP/Azure) administration experience, preferably with a hybrid bare-metal/cloud environment - Strong communication skills, be willing to document systems and processes and collaborate with peers of varying technical expertise - Comfortable operating across the stack, from cluster operations and infrastructure automation to backend service development Requirements - Experience in any of the following will make you stand out: - Developing large-scale production systems with high reliability requirements - Pipeline orchestration frameworks (e.g., Kubeflow, Argo Workflows, Flyte) - Managing GPU workloads on HPC clusters, ideally with hands-on experience in operating NVIDIA’s networking stack (e.g., NCCL, Mellanox firmware, GPUDirect RDMA) - Deployment of services for AI training or inference - Networking fundamentals, including TCP/IP, DNS, routing, load balancing, TLS, and network debugging tools - Maintaining or contributing to open-source projects Benefits - Competitive compensation - Startup equity - Health insurance - Flexibility in terms of remote work - The US base salary range for this full-time position is $200,000 - $290,000 - Individual compensation will be determined by experience, skills, and job-related knowledge Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Role Description In this role, you will design and deliver multi-petabyte storage systems purpose-built for the world’s largest AI training and inference workloads. You’ll architect high-performance parallel filesystems and object stores, evaluate and integrate cutting-edge technologies such as WekaFS, Ceph, and Lustre, and drive aggressive cost optimization—routinely achieving 30-50% savings through intelligent tiering, lifecycle policies, capacity forecasting, and right-sizing. You will also build Kubernetes-native storage operators and self-service platforms that provide automated provisioning, strict multi-tenancy, performance isolation, and quota enforcement at cluster scale. Day-to-day, you’ll optimize end-to-end data paths for 10-50 GB/s per node, design multi-tier caching architectures, implement intelligent prefetching and model-weight distribution, and tune parallel filesystems for AI workloads. Qualifications - 8+ years in storage engineering with 3+ years managing distributed storage at multi-petabyte scale - Proven track record deploying and operating high-performance storage for GPU/HPC clusters - Deep Kubernetes and cloud-native storage experience in production environments - Strong coding skills in Go and Python with demonstrated ability to build production-grade tools - BS/MS in Computer Science, Engineering, or equivalent practical experience - History of technical leadership: designing systems that significantly improved performance (>3x), reliability (99.9%+ uptime), or cost efficiency - Distributed Storage Systems: Deep expertise in WekaFS, Lustre, GPFS, BeeGFS, or similar parallel filesystems at multi-petabyte scale - Object Storage: Production experience with S3, MinIO, Ceph, or R2 including performance optimization and cost management - Kubernetes Storage: CSI drivers, StatefulSets, PersistentVolumes, storage operators, and custom controllers - Storage optimization for GPU workloads, RDMA/InfiniBand networking, parallel filesystem optimization (100+ GB/s aggregate cluster throughput) - Programming: Go and Python for automation, operators, and tooling - Infrastructure as Code: Terraform, Ansible, Helm, GitOps (ArgoCD) - Linux Storage Stack: Advanced knowledge of filesystems (ext4, xfs), LVM, NVMe optimization, RAID configurations - Observability: Prometheus, Grafana, Thanos architecture and operations Requirements - GPU Direct Storage (GDS), NVMe-oF, storage networking (100GbE/400GbE) - ML/AI storage patterns (model weights, checkpointing, dataset caching) - Kubernetes operator development (controller-runtime, kubebuilder) - Storage snapshots, cloning, and thin provisioning - Backup and disaster recovery (Velero, Restic, cross-region replication) - Storage encryption (at-rest and in-transit), security and compliance - Storage benchmarking and profiling tools (fio, iperf3, iostat, blktrace) Benefits - Competitive compensation - Startup equity - Health insurance - Flexibility in terms of remote work Company Description Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Role Description Together AI is rapidly scaling its compute infrastructure across multiple sites and deployment types. The Manager, Infrastructure Strategy & Operations role will be the analytical backbone of the Infrastructure Strategy team, owning the research, benchmarking, and decision frameworks that shape how we source, evaluate, and deploy compute at scale. You will sit at the center of real-time sourcing and vendor decisions, owning the market intelligence, site comparisons, and operational analysis that drive infrastructure strategy. You will produce the domain-specific inputs that inform cross-functional decisions with Finance, Infra Eng, and leadership. Responsibilities - Conduct strategic analysis on how to scale and deploy Together's compute infrastructure, translating complex operational data into clear, actionable recommendations for leadership. - Build dashboards and reporting infrastructure that give the team real-time visibility into compute utilization, infrastructure costs, deployment status, and vendor pipelines. - Identify opportunities to optimize how infrastructure is allocated and operated across workloads through compute utilization analysis. - Develop and maintain comparison frameworks for infrastructure sourcing decisions (own vs. lease, location strategy, vendor selection, site evaluation) and synthesize vendor proposals and market data into decision-ready recommendations. - Run ad hoc analyses to support capacity planning decisions. - Develop tracker to monitor critical compute costs across existing and future providers. - Research and evaluate data center sites and energy sourcing options, comparing power availability, connectivity, permitting timelines, deployment readiness, and reliability. - Champion process improvements across the Infrastructure Strategy function, collaborating cross-functionally with Engineering, Data, and Finance to design AI-native workflows that streamline operations and automate repetitive analysis. - Take on ad hoc analytical projects as priorities evolve, operating with speed and minimal direction. Qualifications - 5+ years of experience in management consulting, business operations, strategy, capacity planning or a similar analytically rigorous role. - Strong quantitative skills with a data-driven approach to decision-making and a track record of structuring ambiguous problems and building analyses from scratch. - Prior experience at a high-growth startup or AI company navigating rapid scaling. - Ability to learn new domains quickly and operate effectively in unfamiliar territory. - Comfort operating with ambiguity and speed; strong judgment on when analysis needs more rigor vs. when a directional answer is sufficient. - Excellent communication skills with the ability to distill complex comparisons into clear, concise recommendations for technical and non-technical audiences. - Proficiency in Excel/Google Sheets, familiarity with SQL or data visualization tools, and fluency with AI productivity tools (e.g., Claude Code, Codex). Requirements - Experience in cloud infrastructure, data center strategy, or GPU/compute procurement. - Familiarity with power markets, energy procurement, or renewable energy economics. - Background in infrastructure, energy, or hardware industries. Benefits - Competitive compensation. - Startup equity. - Health insurance. - Flexibility in terms of remote work. - The US base salary range for this full-time position is: $220-260K + equity + benefits. - Salary ranges are determined by location, level, and role. Individual compensation will be determined by experience, skills, and job-related knowledge. Company Description Together AI is an AI-native cloud company building the infrastructure to make AI faster, cheaper, and more accessible. We're rapidly scaling our GPU footprint: signing our own data center leases, building large-scale clusters, and expanding toward a global owned-infrastructure presence. Our research team has contributed to breakthroughs like FlashAttention, Hyena, and RedPajama, and we co-design across software, hardware, and algorithms to push the frontier of AI efficiency. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Role Description Together AI is scaling its GPU infrastructure rapidly, working with a growing network of compute suppliers. As we expand, we need someone who owns the operational and financial accountability layer of our vendor relationships: - Tracking SLA compliance - Managing credits - Auditing invoices - Ensuring every dollar spent on compute is accurate and accounted for This role sits within the Infrastructure Strategy team and is highly cross-functional, working with infrastructure engineering, finance, and go-to-market teams. When incidents happen, our engineering team produces root-cause analyses; your job is to take that technical detail, build an airtight case for credit claims, and negotiate directly with providers until credits are recovered. You will also partner with GTM and finance to assess the downstream impact of service disruptions and inform how we handle customer-facing commitments. This requires someone with sharp attention to detail, comfort navigating technical documentation, and the persistence to hold vendors accountable. Responsibilities - SLA tracking and credit recovery across all GPU compute and data center suppliers, including monitoring uptime and performance commitments, documenting violations, and driving credit claims to resolution - Invoice review and validation for compute infrastructure contracts, flagging discrepancies and resolving billing issues directly with vendors - Regular audits of vendor contracts and SLA performance to verify accuracy of charges and identify cost recovery opportunities - Using root-cause analyses prepared by the infrastructure engineering team to build the case for SLA credits, then negotiating directly with providers to recover them - Partnering with GTM and finance to assess the downstream impact of supplier service disruptions and provide the data needed to inform customer-facing remediation decisions - Building tracking systems and dashboards for vendor financial data, SLA metrics, and credit status across the supplier portfolio, using modern tooling and AI-assisted workflows where possible - Cross-functional coordination with procurement, legal, and finance to ensure contract terms are properly reflected in billing and that SLA remedies are enforced - Historical spend analysis and cost forecasting to support operating plan development and infrastructure budget planning - Process development for invoice review, SLA monitoring, and vendor financial operations as the function scales Qualifications - 4+ years of experience in vendor operations, technical program management, or contract compliance in a technology infrastructure, cloud, or data center environment - Direct experience managing SLA credit processes, invoice reconciliation, and vendor performance tracking with infrastructure or cloud providers - Extreme attention to detail. You catch discrepancies others miss, whether in an invoice, a vendor SLA report, or a contract clause - Enough technical fluency to read postmortems and incident reports, understand the engineering context, and translate that into a compelling case for credit recovery - Strong negotiation skills and persistence in vendor-facing conversations, especially when disputing charges or arguing for SLA credits - Proficiency with project management and financial tracking tools (e.g., Linear, JIRA, NetSuite, or similar). Comfort using AI tools to accelerate workflows Nice to Have - Experience with GPU compute or cloud infrastructure vendors specifically (colocation providers, cloud service providers, or hardware OEMs) - Background in building vendor operations processes from scratch at a fast-growing company - Familiarity with data center contract structures, including power and cooling pass-throughs, metered billing, and committed-use pricing Benefits - Competitive compensation - Startup equity - Health insurance - Flexibility in terms of remote work - US base salary range for this full-time position: $170-200K + equity + benefits Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Role Description Together AI is building its infrastructure footprint at scale, and this role is central to making that happen. As an Infrastructure Design Engineer, you will own the design, planning, and technical execution of whitespace environments (where servers, storage, and network equipment are deployed) across our AI data center portfolio. You are the in-house expert who ensures that rack layouts, power distribution, cooling strategy, structured cabling, and physical infrastructure design are all built to support the density, redundancy, network and reliability requirements of large-scale AI GPU clusters. You will serve as the lead engineer across our DC portfolio, creating white space designs, reviewing partner and contractor designs, and ensuring plans are executed to spec. You will work closely with the Infrastructure Strategy, Infrastructure Engineering, and Operations teams, as well as external MEP consultants, general contractors, and data center partners. This is a technical role on a small, high-accountability team where your judgment directly shapes our ability to bring capacity online on time and to spec. Responsibilities - Architect HPC clusters by designing whitespace layouts, including rack placement, aisle configuration, hot/cold aisle containment, equipment density, and airflow strategy for high-density GPU deployments. - Collaborate with electrical and mechanical engineers to integrate power and cooling infrastructure into whitespace environments. - Collaborate with Network Engineering to define and validate physical layer requirements (structured cabling, pathway planning, port density) for high-speed AI cluster interconnects, ensuring design compatibility with both physical and logical network architectures. - Advise Data Center build teams/contractors to ensure data center build out matches design and architecture specifications. Provide direction to optimize performance, scalability, and cost-effectiveness. - Develop and maintain CAD/BIM drawings, schematics, capacity planning models, and technical documentation to support site design, construction, operations, and audits of data center white space. - Own capacity planning within the white space through modeling that incorporates growth, utilization, and infrastructure scaling. - Validate electrical and mechanical load distribution across whitespace fit-outs, including review of striping plans and upstream load balancing under normal and failover conditions. - Partner with Infrastructure Strategy and Operations by providing design review and technical guidance during the white space fit-out phase. - Drive continuous improvement in design standards, build processes, and quality assurance, including identifying opportunities to improve infrastructure reliability, reduce time-to-rack, and apply new technologies such as liquid cooling and high-density power architectures. - Ensure all designs comply with applicable standards including TIA-942, Uptime Institute Tier guidelines, ASHRAE thermal recommendations, and relevant local codes and safety regulations. Qualifications - 7+ years of experience in data center design, critical facilities engineering, or infrastructure delivery, with deep technical knowledge across the full stack (power, cooling, network) as it pertains to white space design and implementation. - Demonstrated experience serving as an owner's engineer or resident engineer, including reviewing consultant drawings, managing contractor compliance, and interpreting construction specifications and submittal documents. - Strong working knowledge of how facility electrical (UPS, switchgear) and cooling (CRAC/CRAH) infrastructures interface with, and support, high-density white space systems (PDUs, in-row cooling, CDUs, RPPs). - Proficiency with CAD/BIM tools such as AutoCAD, Revit, or equivalent; ability to produce and review technical drawings, schematics, and capacity models. - Working knowledge of industry standards including TIA-942, Uptime Institute Tier classifications, ASHRAE thermal guidelines, and ANSI/BICSI; familiarity with local building codes and safety regulations. - Proven ability to manage multiple concurrent projects across different sites and work effectively with MEP engineers, general contractors, and equipment vendors in fast-moving environments. - Willingness to travel to data center sites as required for technical due diligence, design inspections, and final white space deployment sign-off. Nice to Have - Experience with liquid cooling systems for high-density GPU clusters, including direct liquid cooling (DLC), immersion cooling, or rear-door heat exchangers, and familiarity with associated power and plumbing infrastructure. - Background in hyperscale or AI-native infrastructure deployments, including reference architecture validation, and the development of final design acceptance criteria at scale. - Familiarity with DCIM platforms, telemetry and monitoring systems, and infrastructure-as-code tooling for capacity and utilization tracking. Benefits - Competitive compensation. - Startup equity. - Health insurance. - Flexibility in terms of remote work. - The US base salary range for this full-time position is: $210-250K + equity + benefits. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Role Description Together AI is building the AI Acceleration Cloud. We are building an end-to-end platform for the generative AI lifecycle, integrating fast, reliable inference and model-shaping services with cutting-edge AI cloud infrastructure. We seek a seasoned Senior Technical Recruiter to collaborate with Engineering leaders to drive hiring for diverse roles within our team. - Partner with executives and engineering leadership to assess current and future hiring needs in core engineering functions. - Manage the candidate journey from sourcing handoff to offer stage, ensuring a seamless and exceptional experience. - Provide market intelligence, funnel metrics, and competitive insights to shape hiring strategies and influence decisions. - Collaborate with sourcers, coordinators, and recruiters to streamline processes and deliver a consistent, brand-aligned candidate experience. - Act as a strategic thought partner to leaders, scaling from concept to execution with data-driven insights, creativity, and sound judgment. Qualifications - 5+ years of technical recruiting experience in high-growth tech environments. - Proven success in hiring for hyperscale startups. - Strong ability to build and maintain relationships with leadership and hiring teams. - You thrive in a fast-paced, ambiguous environment with rapidly shifting priorities. - Expertise in designing new interview processes and hiring strategies from the ground up. - You adapt gracefully under pressure and navigate changing priorities with ease. - Ability to do more with less; you thrive in a scrappy environment and find creative solutions to problems. Benefits - Competitive compensation. - Startup equity. - Health insurance. - Flexibility in terms of remote work. Company Description Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Role Description As a Forward Deployed Engineer (FDE) focused on large scale GPU clusters, you will be a hands-on technical partner to our strategic customers – the world’s leading AI model builders. You will partner with our SAs as a deep-domain specialist in large-scale infrastructure, storage, high-performance networking, and cluster orchestration. As key contributors to the CX, Engineering, and Sales organizations, FDEs add tremendous value by ensuring we can meet the requirements of our most complex POCs, facilitate successful platform adoption for our strategic customers, and guide tailored optimization efforts - directly impacting company growth and the hardening of our core platform. Responsibilities - Cluster Hardening & Validation: Design and execute rigorous pre-handover test suites (NCCL, DCGM, GPU Burn) to ensure clusters are stable under the extreme stress of multi-node training. - Technical Partnership: Act as the primary technical point of contact for model labs, helping them tune their orchestration layer (Kubernetes or SLURM) for maximum throughput. - Infrastructure Optimization: Profile and debug low-level bottlenecks in InfiniBand (IB) fabrics, NVLink topologies, and high-performance storage systems. - Opinionated Onboarding: Build reference designs and "out-of-the-box" configurations for training frameworks to reduce customer time-to-train. - Benchmarking & Migration: Lead complex benchmarking exercises to demonstrate the performance impact of migrating to new hardware families or Together AI’s optimized infrastructure. - Product Feedback Loop: Directly influence our hardware and software roadmap by surfacing edge cases and performance gaps found during customer deployments. Qualifications - Experience: 5+ years in a technical role, with a strong focus on Large-Scale GPU Infrastructure. - Orchestration Mastery: Deep, hands-on experience with Kubernetes (specifically GPU-operator and device plugins) and/or SLURM for workload scheduling. - Networking & Interconnects: Expert knowledge of InfiniBand, RoCE, and NVLink; ability to diagnose network failures that degrade collective communication (NCCL). - Storage Knowledge: Familiarity with parallel file systems (VAST or Weka preferred) and object storage, specifically in the context of large-scale checkpointing. - Benchmarking Skills: Ability to run and interpret training benchmarks and communication tests to validate cluster health and performance. - Coding & Automation: Proficiency in Python and shell scripting; experience with Ansible or similar tools for automated cluster configuration. - Willingness to dive into the customer's stack to solve hard problems and comfortable with the high-stakes, fast-paced environment of frontier model labs. Benefits - Competitive compensation - Startup equity - Health insurance - Flexibility in terms of remote work Company Description Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers on our journey in building the next generation of AI infrastructure.

Role Description We’re hiring a Support Leader to own and scale Together AI’s customer support function across two distinct, technically demanding domains: API Support and GPU Support. You’ll work closely with Together AI’s VP of Customer Experience and partner tightly with SRE, Inference Platform, and Engineering to represent customers internally and drive resolution at speed. This is a player-coach role: you’ll be hands-on in escalations. Our support operation runs 24/7. Our GPU infrastructure customers hold us to high-stakes SLAs on training workloads. Our API customer base spans thousands of PLG and enterprise accounts relying on our serverless and dedicated inference endpoints. Both domains need a leader who can keep pace technically and build the operational muscle to scale. Responsibilities - Team Leadership and Mentorship - Directly manage and develop a team of support engineers and technical account specialists across API Support and GPU Support functions. - Establish clear performance expectations, career growth paths, and a coaching culture leveraged to identify skill gaps and build training programs to close them. - Run structured 1:1s, team reviews, and escalation retrospectives. - Operationalization and Scaling - Assess and overhaul support workflows, SLA frameworks, and escalation playbooks. - Build triage, prioritization, and handoff protocols that allow the team to scale with customer growth without proportional headcount growth. - Define and own support KPIs: SLA attainment, time-to-resolution, escalation rate, CSAT. - GPU Infrastructure Support (Hands-On) - Jump into complex, active GPU infrastructure issues alongside your team. - Investigate NCCL and InfiniBand failures, SSH connection stalls, Kubelet TLS misconfigurations, GPU/RDMA provisioning timeouts, NFS RDMA mount failures, VAST storage failures, network fabric degradation, etc. - Manage high-stakes SLA obligations with GPU cloud customers running multi-thousand-GPU training workloads. - Coordinate closely with SRE and infrastructure engineering on hardware-level issues and cluster bringup. - API and Inference Support (Hands-On) - Own the support surface for Together AI’s API platform: serverless inference, dedicated inference endpoints (self-serve and managed), billing, rate limits, model upload (BYOM), and API authentication. - Represent the team on complex cases: dedicated endpoint startup failures, safetensors validation errors, NFS/storage performance issues on inference clusters, billing disputes and negative-balance enforcement, and rate limit escalations. - Work with the Inference Platform, Commerce, and Product teams to surface patterns and drive fixes upstream. - Escalation and Cross-Functional Partnership - Be the escalation point for your team’s highest-severity customer issues — triage fast, communicate clearly to customers and internal stakeholders, and drive to resolution. - Partner with SRE, Engineering, and Sales on shared priorities. Represent the support team’s perspective in cross-functional planning. - Own the relationship with support tooling vendors and drive improvements to alerting, SLA tracking, and ticket routing. - Customer Feedback Loop - Systematically analyze ticket patterns and surface product and infrastructure gaps to Engineering and Product. Turn support signal into actionable roadmap input. - Build documentation and self-service resources that reduce inbound volume over time. Qualifications - 10+ years of support engineering or technical support leadership experience, with at least 3 years managing a team. - Demonstrated experience leading infrastructure support or cloud operations. You understand how large-scale workloads behave on distributed systems. - Working knowledge of AI infrastructure. You know how APIs work, can reason about latency and throughput issues, and understand the operational surface of a managed inference platform. - Technical depth to be a credible player-coach. Ability to guide engineers through root cause analysis, and bring credibility to customer-facing escalations. - Experience running SLA-driven support operations with real accountability. Familiarity with Pylon or equivalent support ticketing platforms (Zendesk, etc.) and PagerDuty-style alerting systems. - Strong communication skills, especially under pressure. You can write a clear, concise customer-facing update in the middle of a live incident and distill a complex infrastructure issue into a crisp internal escalation. - Startup mindset. You’re comfortable building process where none exists, and you thrive in environments where priorities shift fast. Benefits - Competitive compensation. - Startup equity. - Health insurance. - Flexibility in terms of remote work. - The US base salary range for this full-time position is: $290,000 - $310,000K + equity + benefits. Equal Opportunity Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

• Serve as Together’s representative on data center construction and expansion projects • Oversee all phases of physical infrastructure delivery at data center sites • Manage partner contracts, service level agreements (SLAs), schedules, and change orders • Coordinate with external partners to resolve field issues • Track equipment deliveries from vendors • Provide regular project status reporting to leadership • Drive process improvements across the data center build-out and infrastructure deployment program • Ensure all deployment activities comply with codes, security policies, and safety standards