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Staff Applied AI Inference Engineer

Crusoe· Cloud Engineering
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San Francisco, CA - US$215k - $260k + bonusFull-timePosted 1mo ago

About the role

Role focused on accelerating large language model inference in production, owning end-to-end inference stack optimization from profiling to deployment, with direct collaboration with customer engineering teams.

Responsibilities

  • Bring current inference techniques into production and refine them.
  • Design and optimize serving architectures, including prefill and decode disaggregation, request routing, and related approaches.
  • Work down into the serving stack, from frameworks like vLLM and SGLang to the CUDA kernels underneath, profiling and running in-depth analysis to find and fix performance problems.
  • Adapt and scale optimization methods across many kinds of ML models, with an emphasis on large language models.
  • Profile and tune deployments against clear targets for latency, throughput, and cost, and keep them dependable under real traffic.
  • Tailor deployments to each customer's models and constraints, partnering with their engineering teams to move a workload from an early proof of concept through to a live, well-monitored production service.
  • Build and support the software and product features around the inference stack in a production setting, using one or more general-purpose languages, with Python preferred given how central it is to ML work.
  • Experiment quickly: take fuzzy goals, shape them into clear specs and focused proofs of concept, run fast experiments to find what works, and ship well-tested results without delay.
  • Own delivery end to end, from the first experiment through to the optimization running in production, keeping the underlying performance goals, clear specs, and follow-through front of mind, and drafting features and product requirement documents together with other engineering and product teams.
  • Work through ambiguity and make sound calls on tradeoffs and tooling, steering away from complexity that is not needed.
  • Take real pride and ownership in your work, hold yourself accountable, and look for the same from the people around you.

Qualifications

  • A Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
  • Hands-on experience shipping code in production with one or more general-purpose languages, such as Python or C++, with a strong preference for Python.
  • Familiarity with methods for optimizing LLMs for high throughput / low latency inference.
  • Comfort with modern LLM serving frameworks such as vLLM or SGLang, and with profiling and analyzing performance down to the kernel level.
  • A firm grasp of how GPUs are built and how they behave.
  • Clear interest and hands-on experience with large language models.
  • A working knowledge of AI/ML pipelines and the full path of developing and deploying ML models.
  • Strong communication skills, particularly when explaining hard technical topics to customers and teammates.

Skills

Required:PythonLLM serving frameworksGPU architectureProfiling and optimizationProduction software delivery
Nice to have:C++

Benefits

  • Competitive compensation and equity packages
  • Restricted Stock Units
  • Paid time off, paid holidays & leave of absence programs
  • Comprehensive health, dental & vision insurance
  • Employer contributions to HSA account
  • Paid parental leave
  • Paid life insurance, short-term and long-term disability
  • Professional development & tuition reimbursement
  • Mental health & wellness support
  • Commuter benefits (parking & transit)
  • Cell phone stipend
  • 401(k) Retirement plan with company match up to 4% of salary
  • Volunteer time off
  • Global travel insurance & emergency assistance
  • Daily meals allowance
  • Additional perks & programs specific to location
Last updated Sep 9, 2026. Interested? Apply on the company site.

About Crusoe

The AI factory company | Renewable-powered AI infrastructure

Crusoe provides next-gen AI infrastructure and cloud compute using an energy-first approach. Deploy AI workloads at scale with reliable performance and 24/7 support.

AI infrastructureData centersEdge computingGPU computingRenewable energycloud computeenergy-first approachsetter-30
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