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 infrastructure and 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

What We are Looking For

Strong software engineering fundamentals and hands-on experience building production-grade LLM inference and training infrastructure (ideally from the ground up)

Experience building LLM training/inference internals such as transformers, distributed training, and working on inference libraries like vLLM, SGLang, Megatron

Experience working on RL training frameworks like Slime, veRL, Ray Train, SkyRL

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

Member of Technical Staff - Research Engineer, Post-training

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Preference Model

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