5-7 years of software engineering experience with a focus on ML infrastructure
Hands-on experience building production-grade LLM inference and training systems
Familiarity with RL training frameworks like Slime and Ray Train
Strong understanding of distributed systems and cloud platforms (AWS, GCP)
Experience with container orchestration (Kubernetes)
Proficient in core ML frameworks such as PyTorch or JAX
Ability to communicate technical trade-offs to non-specialists
Responsibilities
Design and scale compute, scheduling, and data infrastructure for post-training research
Develop and maintain core ML framework primitives and internal tools for researchers
Build evaluation, monitoring, and debugging tools to ensure infrastructure reliability
Collaborate with Research Engineers to translate research needs into infrastructure requirements
Implement automated testing and deployment systems for early failure detection
Benefits
Ownership and autonomy in a fast-paced startup environment
Opportunity to work with senior engineers from leading labs and companies
Health, vision, and dental benefits
401K match
Daily onsite lunch and weekly snack orders
Visa sponsorship and relocation support available
Full Job Description
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 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, Miles
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.
