Raydar
Machine Learning Infrastructure Engineer
$200K–$300K
San Francisco, California · Full-time · Posted Oct 26, 2026
- Experience
- No specific number of years stated; open to new graduates with strong internships.
- Education
- Computer science degree from a top undergraduate program; undergraduate study weighted above graduate study.
About the job
Build and operate low-level software and compute infrastructure that improves the speed, throughput, reliability, and cost-efficiency of machine learning workloads across specialized hardware.
What you'll do
- Bring open-source models into production and tune model-prediction software for speed and throughput.
- Write GPU kernels and design, deploy, and operate clusters spanning hardware from multiple vendors.
- Monitor live systems for uptime, performance, and operating costs, and work with customers on their needs.
What they ask for
- Demonstrated exceptional results, such as top competition placement, end-to-end system ownership, or rapid career progression.
- Backend, infrastructure, or systems experience at a leading technology company or early-stage startup.
- Deep understanding of computer architecture, memory hierarchy, and operating system internals.
- Computer science degree from a top undergraduate program; able to start immediately and work on site in San Francisco.
Nice to have
- Experience speeding up and reducing the serving cost of machine learning models; experience operating large multi-machine hardware installations.
- GPU programming exposure, ideally with a common accelerator toolkit.
Pay
$200K - $300K (base salary)
Tools
GPU
AI focus
Use AI agents and coding tools to improve workflow; bring open-source models into production and optimize their performance.
Model-written summary · the original posting is authoritative