Utilidata

• Oversee the deployment and management of containerized applications using Kubernetes, ensuring optimal performance and availability • Contribute to strategic planning regarding how the infrastructure solutions evolve to match the requirements of Data Center partners • Lead the design, implementation, and maintenance of scalable and reliable systems on AWS and/or on-premise • Utilize Terraform for infrastructure as code to automate the provisioning and management of cloud resources • Monitor system performance and uptime, ensuring systems meet established service level objectives (SLOs) • Support SOC2 security compliance requirements for data handling • Mentor and guide team members in DevOps practices, promoting a culture of reliability and excellence • Advocate for automation of operational tasks to enhance efficiency and reduce manual intervention • Collaborate with cross-functional teams to build and maintain CI/CD pipelines • Troubleshoot and resolve complex production issues, conducting root cause analysis and implementing corrective actions • Participate in on-call rotations and incident response teams • Assist in capacity planning, performance tuning, and technical decision-making • Drive continuous improvement initiatives for processes and infrastructure

• Work directly with customer data center operations and engineering teams to assess software environments, lead integration discussions, and translate requirements into actionable designs across DCIM platforms, custom dashboards, hypervisors, and management APIs • Design, develop, and validate custom API connectors between Karman and customer-specific data center software systems • Validate server builds against Karman's software requirements; write and execute configuration checkers and test suites for installation across customer environments • Conduct benchmark-driven and exploratory performance testing to identify and resolve bottlenecks • Debug integration issues and installation failures independently, leveraging deep familiarity with the Karman platform built through close collaboration with internal software engineering teams • Drive execution and sign-off of software installation requirement checklists for each customer engagement • Take an AI-first approach to automating processes and generating test frameworks, configuration checkers, integration artifacts, and deployment documentation • Build the software integration playbook — repeatable processes, templates, and documentation that scale the function beyond individual customer engagements • Create and maintain software integration artifacts, including SW validation checklists, test plans, configuration guides, KPI trackers, and After-Action Reports • Participate in roadmap meetings, engineering design reviews, and sprint planning to maintain a current understanding of the Karman platform • Serve as the feedback loop between customer deployments and core engineering, translating integration learnings into platform improvements • Support the TechOps team with software integration expertise for escalated production incidents related to initial installation

• Architect and contribute directly to core platform components, including ingestion pipelines, transformation frameworks, data models, and orchestration • Define and evolve the multi-quarter technical roadmap for the data platform, balancing new capabilities, reliability investments, and technical debt reduction in alignment with the broader platform architecture • Drive evaluation and adoption of tooling across the stack, ensuring choices are well-reasoned and aligned with where the platform needs to go • Lead architecture reviews and design discussions, ensuring decisions are well-reasoned, documented, and understood by the team • Cut through ambiguity by asking the right questions early about data quality, schema evolution, and downstream dependencies, and identify risks before they become crises • Translate complex data infrastructure decisions for non-technical stakeholders without oversimplifying, and break vague product requirements into clear engineering tasks and acceptance criteria • Partner closely with data science leads and cross-functional teams to surface dependencies and constraints early and prioritize improvements that unlock productivity • Run a lightweight but effective backlog and planning process, keeping the team focused and unblocked • Mentor and grow engineers with an emphasis on raising technical depth — delegate meaningful work, pair on hard problems, and create opportunities for others to stretch • Set code review standards, testing philosophy, and engineering best practices that make the whole team better, including data validation, pipeline testing, and schema management • Ensure data systems work reliably in production — instrumented, observable, and operable, with clear SLAs on freshness, completeness, and accuracy

• Lead and set technical direction for the team responsible for Karman's algorithms, control methods, optimization, and applied research • Drive technical co-development with strategic technology partners across hardware, firmware, and commercial tracks • Develop and manage release plans for algorithms and applications work • Partner with the VP, Engineering to ensure algorithms and control methods are designed for production deployment • Stay hands-on early in the role and scale toward leader-of-leaders as the team grows • Collaborate with the People Team to attract, onboard, and retain top talent in algorithms, applied AI, and control systems • Optimize resource allocation, budgeting, and timelines for team effectiveness • Develop and report on success metrics that measure the team's contribution to organizational objectives

• Execute event strategy and planning for both internal and external events, ensuring alignment with business goals and outcomes • Manage logistics for conferences and tradeshows including venue coordination, vendor management, booth logistics, attendee support, hotel and dinner logistics, and onsite support for large tradeshows • Identify and evaluate industry events that advance Utilidata’s positioning in the data center market; make recommendations on participation and sponsorship opportunities • Negotiate and coordinate sponsorships, speaker opportunities, and exhibitor packages to maximize ROI • Collaborate cross-functionally with sales, product, and leadership teams as needed to ensure event messaging, materials, and outcomes are consistent with strategic objectives • Support internal event execution, as needed, which could include team strategy summits, planning sessions, leadership offsites, or onsite customer meetings and events, ensuring smooth logistics and positive employee and customer experience

• Own GAAP-compliant books and manage the monthly close process • Ensure corporate, sales, and location tax compliance; maintain audit readiness; and monitor cash health • Serve as primary liaison to external auditors and tax advisors • Maintain and strengthen internal controls across financial operations • Manage finance policies and standard operating procedures • Oversee AR/AP, procurement, and credit card compliance • Manage and maintain Carta and the cap table, including option pool tracking, preparing grants for Board approval, and issuing grants • Manage investor reporting requirements (e.g., monthly KPI and financial updates) and coordinate responses to routine investor information requests • Prepare and coordinate Budget vs. Actual reporting; support departmental budget alignment • Ensure documentation retention and finance-related compliance frameworks are maintained and audit-ready • Oversee payroll accounting integration and ensure payroll accuracy with execution owned by the Finance & Accounting Operations Associate

Utilidata is a fast-growing NVIDIA-backed edge AI company enabling greater visibility and control of power utilization in energy-intensive infrastructure, like the electric grid and data centers. Karman, the company’s distributed AI platform powered by a custom NVIDIA module, is transforming the way utility companies operate the grid edge and will enable data centers to unlock more compute for the same provisioned power. The AI Infrastructure Engineer is responsible for designing, building, and owning the end-to-end infrastructure that serves Utilidata's AI and ML models across edge deployments, cloud environments, and data center integrations. They are also responsible for designing, building, and owning the integration of power data with AI inference software. This is Utilidata's first dedicated role of this kind, and will serve as the foundational function for how the company deploys and operates AI capabilities in production. The role requires deep technical expertise in ML model serving, distributed systems, and GPU infrastructure, with a strong emphasis on reliability, performance, and scalability. This position works cross-functionally with product, engineering, and data science teams and is open to fully remote candidates, with periodic travel expected for company retreats and key on-site engagements. Responsibilities - Lead the design and build of Utilidata's AI inference platform — establishing architecture patterns, deployment standards, and operational practices that will scale with the company - Own end-to-end model serving infrastructure for Utilidata's AI infrastructure (on-prem and datacenter) - Build and maintain fault-tolerant, high-performance systems for serving AI models at scale, with a focus on low latency, reliability, and cost efficiency - Collaborate closely with algorithms engineers to integrate AI inference data and configuration with power optimization algorithms - Optimize GPU utilization and inference performance across our hardware fleet, including NVIDIA accelerators central to Utilidata's edge AI platform - Establish MLOps best practices including CI/CD pipelines for model deployment, monitoring, and rollback across environments - Contribute to infrastructure roadmap decisions, including build vs. buy tradeoffs, tooling selection, and platform evolution as the team grows Minimum Qualifications - 5+ years of software engineering experience with a strong focus on AI infrastructure, backend systems, or distributed systems - Hands-on experience with AI model serving frameworks (e.g., vLLM, SGLang, Triton, TensorRT, TorchServe, or similar) - Understanding of container orchestration and cluster management (Kubernetes, Docker) - Experience deploying and operating infrastructure across both datacenter and on-prem environments - Strong knowledge of GPU workloads and the tradeoffs that come with them — you understand how inference differs from training, and why it matters - Proficiency in Python; C++, CUDA, Go, Rust a plus - Excellent communication skills and comfort working cross-functionally in a lean, fast-moving environment - Willingness to travel up to 10% of time Enhanced Qualifications (Nice to Have) - Dynamo experience a plus - Experience with edge AI deployments or constrained compute environments - Familiarity with infrastructure as code (Terraform, Helm) - Experience with observability platforms (Datadog, Prometheus, Grafana) - Background in energy, utilities, or industrial IoT - Contributions to open-source ML infrastructure projects Salary Range: $170,000 to $210,000 base compensation depending on experience plus stock options. Salary will be commensurate with an individual's skills, training, years of experience, and in line with internal compensation bands. Location: This position can be performed remotely from anywhere in the United States. Our Commitments: Utilidata values the diversity of our team. We provide equal employment opportunities without regard to race, color, religion, creed, sex, gender, sexual orientation, gender identity or expression, national origin, age, physical disability, mental disability, medical condition, pregnancy or childbirth, sexual orientation, genetics, genetic information, marital status, or status as a covered veteran or any other basis protected by applicable federal, state and local laws. We are committed to: - Creating a diverse and inclusive workplace that is welcoming, supportive, affirming and respectful - Empowering employees to solve problems and work together to make a difference - Providing mentorship and growth opportunities as part of a collaborative team - A flexible work environment with flexible paid time off - Competitive compensation and benefits, including health, dental, vision, and employer-match 401k

Utilidata is a fast-growing NVIDIA-backed edge AI company enabling greater visibility and control of power utilization in energy-intensive infrastructure, like the electric grid and data centers. Karman, the company’s distributed AI platform powered by a custom NVIDIA module, is transforming the way utility companies operate the grid edge and will enable data centers to unlock more compute for the same provisioned power. Utilidata is seeking a Platform Architect to define and lead the architecture of our Karman data center solution, focused on high frequency waveform acquisition, low latency control paths, and the Karman SoM hardware and software co-design for GPU-dense environments. This role requires deep technical authority across embedded systems, operating systems, firmware, high performance software, and accelerator optimization, with a strong emphasis on deterministic latency and end to end power-aware system design. The Architect will work closely with engineering leadership, hardware, firmware, platform, and data teams to ensure Karman delivers actionable insight and control at data-center scale. Responsibilities - Architecture & Technical Strategy - Own the end-to-end architecture of Utilidata’s Karman data center platform, spanning: - High-resolution waveform sensing and acquisition - Embedded firmware and Yocto based OS layers - Edge compute and control loops - Off device visibility, analytics, and orchestration - Define architectural principles that prioritize determinism, reliability, security, and low latency in power critical environments for production. - Architect systems that integrate GPU telemetry, power states, and control interfaces. - Serve as a technical leader and mentor for sub-system level principal engineers and architects across hardware, firmware, operating system layers, and on-device software teams. - Partner with Product and Customer teams to align architecture with real world data center constraints. - Drive technical reviews, architectural documentation, and long-term roadmap planning. - Translate product and customer requirements into technical designs that meet near term business commitments while establishing a clear path to long term scalability.  - Product Development & Technical Leadership - Architect ultra low latency software paths for waveform capture and processing, real time anomaly detection and closed loop power control - Ensure system designs support predictable response times suitable for power capping and spike mitigation in GPU-dense server racks - Lead performance modeling, instrumentation, and optimization across the full stack. - Provide deep technical leadership in: - Low-level OS internals, kernel scheduling, memory management, and I/O paths - Firmware, bootloaders, BSPs, and hardware abstraction layers - Real-time and near real time execution environments - Partner with hardware teams on sensor integration, signal fidelity, timing accuracy, and compute placement Minimum Qualifications  - 10+ years experience in systems architecture, embedded systems, or high-performance platforms - Prior experience designing and delivering distributed embedded systems from initial architecture through production build and large scale deployment in mission-critical environments - Deep expertise in low level operating systems, firmware, and embedded software - Proven track record designing low latency, high throughput, real time or near real time systems - Strong background in hardware-software co-design, especially in power-constrained or performance critical environments - Experience with performance profiling, tracing, and optimization across CPU, memory, networking, and I/O - Experience with IoT or edge platforms that operate at scale and under strict reliability constraints - Familiarity with real-time scheduling, timing analysis, and deterministic system behavior - Proven ability to lead and influence across teams and organizational levels without formal authority by building trust and driving alignment - Proven ability to communicate effectively across all organizational levels and translate complex information into clear, actionable guidance - Demonstrated ability to lead architecture across multiple teams and disciplines - Willingness to travel up to 20% of time  Enhanced Qualifications (Nice to Have)  - Experience working with GPU based systems or other accelerators, including power and performance tradeoffs - Familiar with NVIDIA SOC architectures and developing productionizable implementations - Experience in data center infrastructure, power systems, or energy aware computing - Familiarity with RDMA based architectures, high speed interconnects, and zero copy data pipelines - Exposure to AI/ML inference pipelines, especially in real time or edge contexts Salary Range: $195,000 to $235,000 base compensation depending on experience.  Salary will be commensurate with an individual's skills, training, years of experience, and in line with internal compensation bands. Location: This position can be performed remotely from anywhere in the United States (preference for candidates based in close proximity to the company's headquarters in Ann Arbor, MI) Our Commitments: Utilidata values the diversity of our team. We provide equal employment opportunities without regard to race, color, religion, creed, sex, gender, sexual orientation, gender identity or expression, national origin, age, physical disability, mental disability, medical condition, pregnancy or childbirth, sexual orientation, genetics, genetic information, marital status, or status as a covered veteran or any other basis protected by applicable federal, state and local laws. We are committed to: - Creating a diverse and inclusive workplace that is welcoming, supportive, affirming and respectful - Empowering employees to solve problems and work together to make a difference - Providing mentorship and growth opportunities as part of a collaborative team - A flexible work environment with flexible paid time off - Competitive compensation and benefits, including health, dental, vision, and employer-match 401k

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a DevOps Engineer to help design, build, and operate Utilidata’s off-device platform that ingests, processes, and serves data flowing from edge AI devices. This is a hands-on development role with technical leadership responsibilities and with company-wide impact. - Oversee the deployment and management of containerized applications using Kubernetes, ensuring optimal performance and availability - Contribute to strategic planning regarding how the infrastructure solutions evolve to match the requirements of Data Center partners - Lead the design, implementation, and maintenance of scalable and reliable systems on AWS and/or on-premise - Utilize Terraform for infrastructure as code to automate the provisioning and management of cloud resources - Monitor system performance and uptime, ensuring systems meet established service level objectives (SLOs) - Support SOC2 security compliance requirements for data handling - Mentor and guide team members in DevOps practices, promoting a culture of reliability and excellence - Advocate for automation of operational tasks to enhance efficiency and reduce manual intervention - Collaborate with cross-functional teams to build and maintain CI/CD pipelines - Troubleshoot and resolve complex production issues, conducting root cause analysis and implementing corrective actions - Participate in on-call rotations and incident response teams - Assist in capacity planning, performance tuning, and technical decision-making - Drive continuous improvement initiatives for processes and infrastructure Qualifications - 8+ years of development experience including extensive experience in platform engineering, SRE, or distributed systems, with clear senior or principal-level impact - Experience designing and operating infrastructure across on-premises and cloud environments - Strong proficiency in container orchestration, particularly Kubernetes - Strong proficiency with AWS services and architecture - Hands-on experience with Terraform for infrastructure automation - Familiarity with monitoring tools (Prometheus, Grafana, or similar) and observability best practices - Excellent problem-solving skills, leadership abilities, and attention to detail - Strong communication and collaboration skills, with experience in driving technical outcomes - Willingness to travel up to 20% of time Enhanced Qualifications (Nice to Have) - Bachelor's degree in Computer Science, Engineering, or a related field - Experience supporting or enabling MLOps platforms, model deployment pipelines, or ML-adjacent infrastructure - AI Workload scheduling using Kubernetes - Knowledge of Apache Spark for large-scale data processing - Knowledge of database technologies (SQL, NoSQL) - Understanding of networking concepts and security best practices Salary Range $180,000 to $210,000 base compensation depending on experience and stock options. Salary will be commensurate with an individual's skills, training, years of experience, and in line with internal compensation bands. Location This position can be performed remotely from anywhere in the United States. Our Commitments - Creating a diverse and inclusive workplace that is welcoming, supportive, affirming and respectful - Empowering employees to solve problems and work together to make a difference - Providing mentorship and growth opportunities as part of a collaborative team - A flexible work environment with flexible paid time off - Competitive compensation and benefits, including health, dental, vision, and employer-match 401k