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Contract Gpu information

What is a contract GPU?

Contract GPU jobs are temporary or project-based positions that require expertise in graphics processing units (GPUs). These jobs often involve developing, optimizing, or deploying GPU-accelerated applications, such as in machine learning, scientific computing, game development, or rendering. Contract workers may be hired by companies for specific tasks that need specialized knowledge in GPU programming using tools like CUDA or OpenCL. These roles can be remote or on-site and typically last for the duration of a particular project or set timeframe.

What are the key skills and qualifications needed to thrive as a contract GPU engineer?

To thrive as a Contract GPU Engineer, you need a strong background in computer science, GPU architecture, and parallel programming, often supported by a degree in a relevant field and prior experience with graphics hardware. Familiarity with tools and frameworks such as CUDA, OpenCL, DirectX, Vulkan, and performance profiling utilities is typically required. Excellent problem-solving, time management, and communication skills are crucial for collaborating with diverse teams and meeting project deadlines. These competencies ensure efficient development, optimization, and deployment of GPU-accelerated solutions in dynamic contract environments.

What are some common challenges faced by professionals working in contract GPU roles, and how can they address them?

Professionals in contract GPU roles often encounter challenges such as rapidly evolving hardware and software standards, tight project timelines, and the need to quickly adapt to different team environments. Staying updated with the latest GPU technologies and frameworks is essential, as is developing strong communication skills to collaborate effectively with full-time team members and stakeholders. To address these challenges, leveraging online resources, participating in relevant forums, and proactively seeking feedback can help contract GPU specialists deliver high-quality results and integrate smoothly into diverse project teams.

What is the difference between Contract Gpu vs Contract Data Scientist?

AspectContract GpuContract Data Scientist
Required CredentialsGPU certifications, technical skills in GPU programmingStatistics, programming, data analysis certifications
Work EnvironmentTech companies, research labs, AI firmsTech firms, finance, healthcare, consulting
Employer & Industry UsageAI development, machine learning projectsData analysis, predictive modeling, business insights

Contract Gpu roles focus on GPU hardware and software expertise for AI and machine learning projects, while Contract Data Scientist roles emphasize data analysis, statistical modeling, and insights. Both are in tech-driven industries but serve different technical functions.

What are the most commonly searched types of Gpu jobs in Michigan?

The most popular types of Gpu jobs in Michigan are:

What are popular job titles related to Contract Gpu jobs in Michigan?

For Contract Gpu jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Contract Gpu jobs in Michigan look for?

The top searched job categories for Contract Gpu jobs in Michigan are:

What cities in Michigan are hiring for Contract Gpu jobs?

Cities in Michigan with the most Contract Gpu job openings:

Contract Gpu jobs near you

Principal Architect, Simulation Platform

Utilidata

Ann Arbor, MI • On-site, Remote

$200K - $240K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 21 days ago

Job description

Utilidata is a fast-growing NVIDIA-backed AI company enabling AI data centers to dynamically orchestrate power and unlock more compute capacity from existing energy infrastructure. For over a decade, we have applied AI to the electric grid - bringing real-time visibility and power-flow control to complex energy infrastructure. Our Karman platform, built on a custom NVIDIA module, brings that same capability to AI data centers, giving operators a way to better use the power already available to them.
We are building the Karman Digital Environment (KDE), a simulation platform that will evolve how Karman is developed, verified, and brought to market. Today, Karman is developed and verified against shared physical systems, including custom NVIDIA-based edge compute units that process sub-millisecond power waveform data and issue real-time control signals to GPU infrastructure. This is a hardware constraint that limits how fast teams can iterate. KDE will remove that constraint, enabling simulation-driven development at scale across every layer of fidelity, from rapid experimentation to high-confidence system validation. This is a technical leadership role reporting to the VP, AI & Applications, with company-wide impact across architecture, product, and customer-facing teams.
Responsibilities
  • Own the end-to-end architecture of KDE as the simulation platform for developing, testing, and validating Karman before production deployment.
  • Architect systems spanning heterogeneous time domains - from sub-millisecond control paths to system-level orchestration - with coherent time management and deterministic behavior across both.
  • Design and evolve the core simulation platform: time synchronization across paced (real-time SIL) and unpaced (batch) execution modes, signal routing and data flow orchestration, deterministic replay and scenario execution, and the platform APIs and data contracts consumed by algorithm, QA, product, and GTM teams.
  • Define and maintain the platform's performance envelope - latency, jitter, throughput, and fidelity - and lead optimization across the full stack.
  • Serve as the technical authority for simulation and validation methodology, partnering with algorithm, QA, product, hardware, firmware, and GTM teams to align platform capabilities with real-world Karman deployments.
  • Build and lead an engineering team spanning real-time systems, simulation infrastructure, power systems modeling, and GPU calibration, while remaining deeply hands-on in architecture and critical technical decisions.
  • Represent KDE on a standing cross-functional council with Algorithms, Product, GTM, and QA, arbitrating priorities across the platform's customers and maintaining architectural direction with senior peers.
Minimum Qualifications
  • Bachelor's or advanced degree in Electrical Engineering, Computer Engineering, Computer Science, Robotics, Physics, or a related field
  • 10+ years in systems architecture, simulation platforms, real-time or hardware-in-the-loop systems, embedded systems, or high-performance distributed platforms
  • Experience as a hands-on player-coach leading small, high-performing engineering teams
  • A proven track record designing and delivering simulation platforms or large-scale test systems that other engineering teams treat as production hardware, including data ingestion, telemetry, and playback at production scale, and the ability to design platform APIs and data contracts used by multiple teams
  • Deep expertise in system-level behavior - scheduling, concurrency, memory access patterns, latency budgets, jitter measurement, and determinism at sub-millisecond scale
  • Substantive fluency in the physics of the simulated domain - for this role, AC power distribution, control-loop dynamics, and electrical protection systems - is sufficient to make architectural decisions independently. Candidates from adjacent domains (automotive powertrain, aerospace propulsion, grid simulation, robotics dynamics) who've reached comparable depth will also be considered
  • Experience leading or contributing to a novel modeling problem in the platform's domain
  • Strong communication skills, with the ability to translate complex system designs for technical and non-technical stakeholders alike
  • Willingness to travel up to 25% of time
Enhanced Qualifications (Nice to Have)
  • Experience with GPU-based systems or other accelerators, including power and performance tradeoffs
  • Experience in data center power infrastructure or hyperscaler energy-aware computing
  • Familiarity with AI inference serving systems (vLLM, TensorRT-LLM, Triton) sufficient to reason about workload-driven power dynamics
Salary Range: $200,000 to $240,000 base compensation depending on experience and level, plus stock options. This role spans multiple job levels; final level and compensation will be determined based on the candidate's experience.
Location: This position can be performed remotely from within the United States. Preference will be given to candidates based in or around the Bay Area (CA) or Ann Arbor, MI. Periodic travel to the company's HQ in Ann Arbor, MI is required.
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

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