About the RoleFrontier 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
SeniorML 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.
What You Will Do- 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
- Proficiency in core ML frameworks such as PyTorch or JAX
- Can balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists
What We Offer:- 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. 
