The roleAs a platform research engineer, you'll build the core AI systems that make Applied Compute's platform intelligent. This includes our memory system, continual learning infrastructure, agent-building tooling, scaling synthetic environments & evaluations, and integrating the RL stack that powers agent improvement over time. You'll 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 build ML-grounded platform capabilities that make every delivery better.
What you'll do- Build and improve the memory refinery: 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 initial 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
- Experience building systems that involve LLMs in production: prompt engineering, structured outputs, RAG, or agent frameworks
- Clear research experience: including 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, RLHF, or similar human-in-the-loop learning systems
- Background in building evaluation frameworks, benchmarks, or data quality systems
- Experience with continual learning, memory systems, or knowledge 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
Benefits & LogisticsThis role is based in San Francisco. We work from our office in the Mission. We offer:
- Competitive compensation and equity
- Generous health benefits
- 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.
