Role description
Applied ComputeThe role
As a research scientist, you will design, implement, and optimize the large-scale training infrastructure that powers our frontier reinforcement learning stack. This is systems work at the edge of what's possible, training state-of-the-art models for our enterprise partners. Frontier systems are exciting but brittle, and require both performance and correctness to train models effectively. You'll work closely with researchers to make our RL stack reliable, fast, and capable of running for days without intervention.
What you'll do
Design and optimize our RL training and inference pipelines across large GPU clusters
Build tooling and observability that lets researchers and customers inspect, profile, and debug training runs
Implement systems with an eye toward how they affect ML (low precision numerics, distributed training edge cases, etc.)
Partner with researchers to bring frontier post-training capabilities into production deployments
What we're looking for
Experience programming with and managing training jobs on large-scale GPU systems
Fearlessness and curiosity to understand all levels of the training stack
Bias toward fast implementation, paired with a high bar for reliability and efficiency
Familiarity with open-weights models (architecture and inference)
Background in reinforcement learning or integration of inference with RL training loops
Strong candidates also have
Experience with distributed training frameworks (PyTorch, JAX, DeepSpeed)
Background in high-performance computing or working with large-scale clusters
Contributions to open-source ML infrastructure
Demonstrated technical creativity through published projects, OSS contributions, or side projects
The U.S. base salary range for this full-time position is $200,000–$335,000. Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a comprehensive benefits package designed to support you both personally and professionally. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.
Benefits & Logistics
This role is based in San Francisco. We work from our office in the Mission. We offer:
Competitive compensation and equity
100% employer-paid health insurance coverage for you and your family
Unlimited PTO
Paid parental leave
Daily lunches and dinners
Transportation and relocation support
5% 401(k) match
We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the process with you. We encourage you to apply even if you do not believe you meet every single qualification. As set forth in Applied Compute’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.