Member of Technical Staff, Performance and Scale
External
Inferact · San FranciscoPrepare for this interview
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About the role
We're looking for an infrastructure engineer to build the distributed systems that power inference at global scale. You'll design and implement the foundational layers that enable vLLM to serve models across thousands of accelerators with minimal latency and maximum reliability. Tomorrow, deploying a frontier model at scale should be as straightforward as spinning up a serverless database. The complexity doesn't disappear as it gets absorbed into the infrastructure you're building.
Requirements
- Bachelor's degree or equivalent experience in computer science, engineering, or similar.
- Strong systems programming skills in Rust, Go, or C++.
- Experience designing and building high-performance distributed systems at scale.
- Understanding of network protocols and high-performance I/O.
- Ability to debug complex distributed systems issues.
- Experience with ML serving infrastructure and disaggregated inference architecture.
- Familiarity with GPU programming models and memory hierarchies.
- Knowledge of GPU interconnects (NVLink, InfiniBand, RoCE) and their performance characteristics.
- Track record of improving system reliability and performance at scale.
- Bonus points if you have:
- Prior experience in supporting large‑scale model training or inference environments.
- Logistics
- Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.
- Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
- Visa sponsorship: We sponsor visas on a case-by-case basis.
- Benefits : Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.
Benefits
Additional Information
Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware-a position that took years to build.
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