Member of Technical Staff - ML Infrastructure Engineer, Post-training
Seattle, Washington, United StatesAll locationsSeattle, Washington, United StatesSan Francisco, California, United States On-site
$180,000–$300,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
Frontier research moves only as fast as its infrastructure permits. Building solid infrastructure is foundational to our mission of pushing self-directed learning as far as it can go. We are looking for Senior ML Infrastructure Engineers to build the systems that power the frontier of post-training on large language models . This role involves building scalable infrastructure to enable high-throughput systems and shape how our research is run, bringing us closer to models that can train themselves on what they aren't yet good at.
What you'll bring
- Significant experience and understanding of distributed systems principles, and have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high-throughput, low-latency workloads
- Have experience with data engineering tools and building robust, scalable data pipelines
- Experience working on RL training frameworks like Slime, veRL, Ray
- Have some familiarity with LLM training/inference internals (transformers, distributed training, inference libraries like vLLM or SGLang)
- Can balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists
- Have strong software engineering fundamentals, experience building production-grade infrastructure, and proficiency in core ML frameworks such as PyTorch or JAX
- ideally for ML or data-intensive systems
- deep expertise is a plus, not a requirement