Job description

THE ROLE As a research systems engineer, you'll train frontier-scale models and develop the methods that make continual learning work inside enterprise environments. You'll design and run experiments at scale, explore cutting-edge RL techniques, and build the tools that let us understand what's actually happening during training. This role sits at the intersection of research and systems. You'll invent new algorithms alongside researchers, then work with infrastructure engineers to run them on GPUs. What you'll do - Post-train frontier-scale language models on enterprise tasks and environments - Explore and develop RL techniques, co-designing algorithms and systems - Contribute to Alchemy, our data research program for generating signal-rich training environments from production data - Build high-performance internal tools for probing, debugging, and analyzing training runs - Partner with infrastructure engineers to scale training and inference efficiently What we're looking for - Experience training or serving large language models - Experience building RL environments and evaluations for language models - Proficiency in PyTorch, JAX, or similar ML frameworks, with experience in distributed training - Strong experimental design skills — you know how to set up experiments that actually answer questions Strong candidates also have - Background in pre-training or post-training research - Previous experience in high-performance computing environments or large-scale clusters - Contributions to open-source ML research or infrastructure - Demonstrated technical creativity through published research, OSS contributions, or side projects The U.S. base salary range for this full-time position is $190,000–$310,000. 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. ABOUT US Applied Compute builds Specific Intelligence for the enterprise. We provide the continual learning infrastructure for companies to build agent workforces trained on proprietary data and institutional expertise. Our researchers and platform embed directly within customer environments to build custom evals, train models, and deploy agents that get better with use. - Why we’re excited: We get to work at a rare intersection. Our product team builds the platform powering a new generation of digital coworkers. Our research team pushes the frontier of post-training and reinforcement learning. Our applied AI team sits side-by-side with customers as they ship agents into production. This combination of strong product, deep research, and boots on the ground is what we believe it takes to bring AI to the enterprise. We are product-led, research-enabled, and forward-deployed. - Who we are: We’re a team of engineers, researchers, and operators. Many of us are former founders. We've built RL infrastructure at OpenAI, data foundations at Scale AI, and systems at Together, Two Sigma, Watershed, and others. We work with F50 customers and are fortunate to be backed by partners like Kleiner Perkins, Benchmark, Sequoia, Lux, and Greenoaks. - Who Thrives Here: We're looking for people who are excited about applying novel research and complex systems to real-world problems. Our team genuinely enjoys working with customers: listening, empathizing, and understanding how work actually gets done in their organizations. Former founders, people who've built a lot of side projects, or anyone who's shown they can own something end-to-end, tend to do well here. 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% 401k 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.

Requirements

Must-haves

Post-train frontier-scale language models on enterprise tasks and environments
Explore and develop RL techniques, co-designing algorithms and systems
Contribute to Alchemy, our data research program for generating signal-rich training environments from production data
Build high-performance internal tools for probing, debugging, and analyzing training runs
Partner with infrastructure engineers to scale training and inference efficiently
Experience training or serving large language models
Experience building RL environments and evaluations for language models
Proficiency in PyTorch, JAX, or similar ML frameworks, with experience in distributed training
Strong experimental design skills — you know how to set up experiments that actually answer questions
Strong candidates also have
Background in pre-training or post-training research
Previous experience in high-performance computing environments or large-scale clusters
Contributions to open-source ML research or infrastructure
Demonstrated technical creativity through published research, OSS contributions, or side projects
Competitive compensation and equity
Daily lunches and dinners
Transportation and relocation support

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlai

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

401k

Pto

Parental Leave