Crusoe

Applied AI Inference Engineer

San Francisco, CA - US

Sponsorship not specified$250k-$300kDetected 14 hours ago

PythonC++DockerMachine LearningLLMsCommunicationProblem Solving

About the role

You will spend your time making large language models run faster, cheaper, and more reliably in production.
That means owning the inference stack end to end: profiling where time and cost go, bringing modern optimization techniques into real deployments, and getting deep into the serving code when the defaults are not good enough.
This is core systems and performance work on some of the most demanding models in use today.

The optimizations you build land in real customer deployments, each with its own models, traffic patterns, latency targets, and cost constraints.
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.
Design and optimize serving architectures, including prefill and decode disaggregation, request routing, and related approaches.

A Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
Familiarity with methods for optimizing LLMs for high throughput / low latency inference.
Clear interest and hands-on experience with large language models.

A track record of making software systems run faster, especially for large language models.
Experience with CUDA or comparable technologies.
Experience with Docker and Kubernetes.
Bring current inference techniques into production and refine them.
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.

Crusoe is on a mission to accelerate the abundance of energy and intelligence.
The demand for AI compute is boundless, and power is a bottleneck.
Comfort with modern LLM serving frameworks such as vLLM or SGLang, and with profiling and analyzing performance down to the kernel level.

Compensation will be paid in the range of up to $250,000 - $300,000 + Bonus.
Compensation to be determined by the applicant's knowledge, education, and abilities, as well as internal equity and alignment with market data.

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.
A firm grasp of how GPUs are built and how they behave.
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.

Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran st

This listing is sourced directly from Crusoe's careers page and normalized into a canonical job model.