The roleAs 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.
Benefits & LogisticsThis 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
- Unlimited PTO
- Paid parental leave
- Daily lunches and dinners
- Transportation and relocation support
- Retirement plans
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.
