Job description

THE ROLE As a platform research engineer, you'll do research around ways to make Applied Compute's platform intelligent. This includes how we set up continual learning against production traces (OPSD, SDPO, RMSD), scaling synthetic environments, data & evaluations, and integrating the RL stack that powers agent improvement over time. Your research will serve as the connective tissue between our AI product engineers (who build the interfaces and tools customers use) and our applied research engineers (who work directly with customers to ship agents into production). Your job is to take learnings from across customer deployments, identify the greatest common denominators, and pursue research to accelerate all of our deliveries. What you'll do - Build and improve our hinting primitives for online self-distillation: the algorithms and systems that allow agents to learn continuously from production traces and company data - Develop and maintain trace search, trace mining, and data labeling systems that feed the continual learning loop - Research and implement approaches to multi-agent system design, including structuring agent coordination and managing trade-offs - Build best in class coding agents for automatically creating agents for delivery and hill-climbing harnesses - Translate learnings from applied research and customer deployments into generalizable platform features - Collaborate closely with AI product engineers to ensure new ML capabilities are integrated into polished platform experiences What we're looking for - Strong software engineering fundamentals combined with deep ML/AI knowledge - Production research experience: both deliveries train models into production & top-tier conference publications, blogs, or reports - Strong experimental design skills: you are diligent, don't cut corners, and actually run experiments - Highly organized: you manage complexity across multiple workstreams - Ability to read and translate research papers and prototypes into shippable engineering Strong candidates also have - Experience with reinforcement learning - Background in building evaluation frameworks, benchmarks, or data quality systems - Experience with continual learning/distillation - Opinions about multi-agent system architectures and the trade-offs between different approaches - Published work or open-source contributions in AI/ML systems - Previous experience as a founder or early engineer at a zero-to-one company 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. 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

Build and improve our hinting primitives for online self-distillation: the algorithms and systems that allow agents to learn continuously from production traces and company data
Develop and maintain trace search, trace mining, and data labeling systems that feed the continual learning loop
Research and implement approaches to multi-agent system design, including structuring agent coordination and managing trade-offs
Build best in class coding agents for automatically creating agents for delivery and hill-climbing harnesses
Translate learnings from applied research and customer deployments into generalizable platform features
Collaborate closely with AI product engineers to ensure new ML capabilities are integrated into polished platform experiences
Strong software engineering fundamentals combined with deep ML/AI knowledge
Production research experience: both deliveries train models into production & top-tier conference publications, blogs, or reports
Strong experimental design skills: you are diligent, don't cut corners, and actually run experiments
Highly organized: you manage complexity across multiple workstreams
Ability to read and translate research papers and prototypes into shippable engineering
Strong candidates also have
Experience with reinforcement learning
Background in building evaluation frameworks, benchmarks, or data quality systems
Experience with continual learning/distillation
Opinions about multi-agent system architectures and the trade-offs between different approaches
Published work or open-source contributions in AI/ML systems
Previous experience as a founder or early engineer at a zero-to-one company
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

aiml

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

401k

Pto

Parental Leave