PRAGMATIKE

• Lead implementation of PCI DSS controls across infrastructure and application layers • Design and secure AWS infrastructure using best practices around IAM, networking, and encryption • Own Infrastructure as Code and improve deployment reliability across environments • Integrate security into CI/CD pipelines and engineering workflows • Improve platform scalability, observability, reliability, and fault tolerance • Manage security tooling, monitoring, logging, and incident readiness processes • Partner with auditors and internal stakeholders on compliance initiatives • Define cloud security and infrastructure best practices across the engineering team • Mentor engineers on secure infrastructure and development practices • Contribute to long-term platform architecture and operational strategy

• Build and scale backend services and APIs powering the core product • Design systems that handle real-time processing and high-throughput workloads • Improve performance, reliability, and scalability of the platform • Work on data flows, async processing, and system integrations • Contribute to infrastructure and deployment pipelines • Use AI coding tools (Cursor, Claude Code, etc.) to accelerate development and iteration • Collaborate closely with engineers and founders on architecture and technical decisions

Role Description We’re looking for a Fullstack Engineer who is a strong builder and comfortable working across the stack, with a modern AI-assisted development workflow (e.g. Cursor, Claude Code, or similar tools). This is a hands-on, product-focused role — you’ll be shipping features end-to-end, contributing to both frontend and backend systems, and leveraging AI tools to move fast and build efficiently. You’ll work closely with the founders to scale the platform, improve user experience, and integrate AI-driven capabilities into the core product. Key Responsibilities - Build and ship end-to-end product features across frontend and backend - Develop scalable backend services and APIs - Contribute to frontend applications with a strong focus on UX and performance - Integrate AI-powered features into the product (LLMs, workflows, automation) - Use AI coding tools (Cursor, Claude Code, etc.) to accelerate development and productivity - Improve and maintain data pipelines (email, LinkedIn, CRM integrations) - Optimize onboarding flows and drive fast “aha moments” for users - Collaborate on system design, architecture, and technical decisions - Ensure performance, reliability, and scalability of the platform - Work closely with founders on product direction and iteration cycles Qualifications - 3–8+ years of experience in software engineering (fullstack or backend-leaning) - Strong ability to build and ship products end-to-end - Comfortable working across the stack (language-agnostic mindset) - Experience using modern AI coding tools (e.g. Cursor, Claude Code, Copilot, etc.) - Experience integrating or building AI/LLM-powered features - Solid understanding of APIs, system design, and scalable architectures - Experience with cloud environments (AWS or similar) - Familiarity with data pipelines and third-party integrations - Strong ownership, autonomy, and product mindset - Ability to thrive in a fast-paced startup environment Nice to Have - Experience with graph-based systems or recommendation engines - Exposure to Graph RAG, knowledge graphs, or AI agents - Experience in fintech, marketplaces, or data-heavy products - Experience with CRM, email, or LinkedIn integrations - Background in early-stage or startup environments - Track record of shipping products from 0 → 1 or scaling early products Benefits - Founding-level role with high impact on company direction - Full ownership of the AI and Graph platform development - Opportunity to build a large-scale AI-driven network platform - Highly autonomous and fast-moving environment - Fully remote setup with flexible work style

• Design and develop scalable, distributed, server-side applications and microservices using Go. • Build and optimize mission-critical backend software, with a strong focus on system programming, concurrency, and performance tuning. • Design and implement robust peer-to-peer (P2P) communication systems (P2P experience is mandatory). • Contribute to or build systems using libp2p or similar P2P frameworks. • Ensure cross-platform compatibility and consistent application behavior across environments. • Collaborate within an Agile/Scrum team, contributing to backlog refinement, risk identification, and dependency management. • Produce clean, maintainable, and reusable code, following strong engineering standards. • Optimize backend systems for performance, reliability, security, and fault tolerance. • Establish and enhance backend infrastructure, including deployment pipelines, observability, and monitoring tooling. • Work across multi-cloud environments (AWS, Azure, GCP) and collaborate closely with infrastructure teams. • Participate actively in the full application lifecycle, from design to production operations. • Engage with product and design teams to deeply understand end-user and platform requirements. • Conduct thorough code reviews, mentor other engineers, and raise overall backend engineering quality. • Champion best engineering practices and a culture of high-quality software craftsmanship.

• Design and develop scalable, distributed, server-side applications and microservices using Go. • Build and optimize mission-critical backend software, with a strong focus on system programming, concurrency, and performance tuning. • Design and implement robust peer-to-peer (P2P) communication systems (P2P experience is mandatory). • Contribute to or build systems using libp2p or similar P2P frameworks. • Ensure cross-platform compatibility and consistent application behavior across environments. • Collaborate within an Agile/Scrum team, contributing to backlog refinement, risk identification, and dependency management. • Produce clean, maintainable, and reusable code, following strong engineering standards. • Optimize backend systems for performance, reliability, security, and fault tolerance. • Establish and enhance backend infrastructure, including deployment pipelines, observability, and monitoring tooling. • Work across multi-cloud environments (AWS, Azure, GCP) and collaborate closely with infrastructure teams. • Participate actively in the full application lifecycle, from design to production operations. • Engage with product and design teams to deeply understand end-user and platform requirements. • Conduct thorough code reviews, mentor other engineers, and raise overall backend engineering quality. • Champion best engineering practices and a culture of high-quality software craftsmanship.

• Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent • Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models • Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers • Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance • Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health • Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments • Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities • Define engineering best practices and contribute to platform scalability in a fast-moving startup environment

• Design and develop scalable, distributed, server-side applications and microservices using Go. • Build and optimize mission-critical backend software, with a strong focus on system programming, concurrency, and performance tuning. • Design and implement robust peer-to-peer (P2P) communication systems (P2P experience is mandatory). • Contribute to or build systems using libp2p or similar P2P frameworks. • Ensure cross-platform compatibility and consistent application behavior across environments. • Collaborate within an Agile/Scrum team, contributing to backlog refinement, risk identification, and dependency management. • Produce clean, maintainable, and reusable code, following strong engineering standards. • Optimize backend systems for performance, reliability, security, and fault tolerance. • Establish and enhance backend infrastructure, including deployment pipelines, observability, and monitoring tooling. • Work across multi-cloud environments (AWS, Azure, GCP) and collaborate closely with infrastructure teams. • Participate actively in the full application lifecycle, from design to production operations. • Engage with product and design teams to deeply understand end-user and platform requirements. • Conduct thorough code reviews, mentor other engineers, and raise overall backend engineering quality. • Champion best engineering practices and a culture of high-quality software craftsmanship.

Role Description - Design, implement, and maintain storage arrays (Compellent, 3PAR, NetApp) to meet organizational needs. - Conduct capacity planning for storage resources, optimize performance, and troubleshoot storage-related issues. - Plan and execute data migrations between storage arrays, ensuring minimal downtime. - Implement and maintain security measures and access controls for data security and compliance. - Stay current on updates and patches, executing timely patch management for storage systems. - Create and maintain documentation for storage configurations and provide training and support to other teams. - Design and implement backup solutions using Avamar, Data Domain, and ECS, aligning with business continuity requirements. - Develop, manage, and update backup policies to ensure compliance and meet evolving business needs. - Monitor backup jobs, address failures, and generate/review backup reports. - Develop and test disaster recovery plans for backup processes, collaborating with other teams to ensure an effective strategy. - Implement processes for verifying backed-up data integrity and conduct periodic data recovery tests. - Participate in incident response for data loss or corruption, working with teams to restore data quickly. - Manage vendor relationships for support and stay informed on relevant updates and patches. Qualifications - At least 5 years of experience in a similar role. - Proven experience in storage and backup administration. - Strong understanding of data security and compliance requirements. - Ability to work independently and as part of a team. - Excellent communication and problem-solving skills.

• Run and optimize enterprise storage and backup platforms across distributed environments • Provision storage, manage integrations, and configure backup policies (scheduling, retention, recovery) • Monitor system health, capacity, and performance — and proactively resolve bottlenecks • Troubleshoot across the stack (storage, SAN, backup, network) and drive incidents to resolution • Operate and improve SAN environments (Fibre Channel, iSCSI, multipathing) • Manage and support multi-vendor storage arrays (e.g., 3PAR, Dell, NetApp) • Administer enterprise backup platforms (e.g., Avamar, Data Domain, NetBackup, Veeam) • Partner with vendors for hardware lifecycle management and issue resolution • Contribute to infrastructure automation (Python, Ansible, scripting) • Support rollout of new storage/backup solutions and infrastructure upgrades • Produce clear technical documentation and operational runbooks • Participate in on-call rotations and ensure production reliability • Step into ownership when needed — including leading operations in the absence of team leadership

Location: Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers. We are searching for a CUDA Kernel Engineer who has hands-on experience developing and optimizing NVIDIA CUDA kernels from scratch. You will work on the GPU performance layer powering large-scale, high-throughput AI systems used by Fortune 500 customers. This role is ideal for someone who deeply understands NVIDIA GPU architecture, memory hierarchy, warp-level execution, and profiling workflowsnot someone coming from generic hardware, FPGA, or non-NVIDIA compute backgrounds. You will directly influence the GPU efficiency, throughput, and scalability of mission-critical AI systems. What Youll Do - Design, implement, and optimize custom CUDA kernels for NVIDIA GPUs, with a focus on maximizing occupancy, memory throughput, and warp efficiency. - Profile GPU workloads using tools such as Nsight Compute, Nsight Systems, nvprof, and CUDA‐MEMCHECK. - Analyze and eliminate performance bottlenecks including warp divergence, uncoalesced memory access, register pressure, and PCIe transfer overhead. - Improve GPU memory pipelines (global, shared, L2, texture memory) and ensure proper memory coalescing. - Collaborate closely with AI systems, model acceleration, and backend distributed systems teams. - Contribute to GPU architecture decisions, kernel libraries, and internal performance-engineering best practices. What Were Looking For - Proven track record building NVIDIA CUDA kernels from scratchnot just calling existing libraries. - Strong ability to optimize kernels (tiling strategies, occupancy tuning, shared memory design, warp scheduling). - Deep understanding of CUDA threads, warps, blocks, and grids, GPU memory hierarchy and memory coalescing, as well as warp divergence (how to detect, analyze, and mitigate it) - Experience diagnosing PCIe bottlenecks and optimizing host-device transfers (pinned memory, streams, batching, overlap). - Familiarity with C++, CUDA runtime APIs, and GPU debugging/profiling tooling. Bonus Points - Experience with multi-GPU or distributed GPU systems (NCCL, NVLink, MIG). - Background in GPU acceleration for ML frameworks or HPC workloads. - Knowledge of model inference optimization (TensorRT, CUDA Graphs, CUTLASS). - Exposure to compiler-level optimization or PTX/SASS analysis. - Startup experience or comfort working in fast-moving, ambiguous environments. Why This Role Will Pivot Your Career - Research pedigree: MIT CSAIL founders recognized for breakthrough AI and systems contributions. - Customer impact: Deploy AI solutions powering Fortune 500 clients. - Industry momentum: Lab alumni have led high-value acquisitions (MosaicML Databricks, Run:AI Nvidia, W&B CoreWeave). - Funding & growth: Oversubscribed seed round, next funding in 2026. - Career growth & influence: Lead AI initiatives, optimize pipelines, and directly impact production AI systems at scale. - Culture & autonomy: Own critical systems while collaborating with world-class engineers. - Aspirational impact: Solve GPU/AI performance challenges few engineers ever face. Benefits - Competitive salary & equity options - Sign-on bonus - Health, Dental, and Vision - 401k Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.