Member of Technical Staff - ML Infrastructure Engineer, Post-training

Senior ML Infrastructure Engineers

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.

Design, build, and scale the compute, scheduling, and data infrastructure that powers post-training research on our in-house RL environments

Develop and maintain core ML framework primitives and internal tooling that researchers rely on daily, accelerating reproducible experimentation and reducing time from idea to result

Build evaluation and benchmarking infrastructure, monitoring, logging, and debugging tooling, and automated testing and deployment systems, so failures are caught early and infrastructure stays reliable as it scales

Partner directly with Research Engineers to translate research needs into infrastructure requirements, and ship fast in response to their feedback

Have strong software engineering fundamentals, experience building production-grade infrastructure (ideally for ML or data-intensive systems), and proficiency in core ML frameworks such as PyTorch or JAX

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); deep expertise is a plus, not a requirement

Can balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists

Competitive cash and equity compensation (>90th percentile)

Ownership and autonomy in a fast moving startup environment

Opportunity to work alongside senior and staff engineers from frontier labs and infrastructure companies, plus top ML engineers

Health, vision, dental, benefits

401K match

Lunch provided everyday onsite

Weekly snack orders

Visa sponsorship & relocation support available

We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.