Platform Research Engineer
Applied Compute7 months ago
Responsibilities
- Build and improve memory refinery algorithms and systems that enable agents to learn continuously from production traces and company data.
- Develop and maintain trace search, trace mining, and data labeling systems for the continual learning loop.
- Research and implement multi-agent system designs, including agent coordination and trade-off management.
- Build coding agents that automatically create initial agents for delivery and hill-climbing harnesses.
- Translate applied research and customer deployment learnings into generalizable platform features.
- Collaborate with AI product engineers to integrate ML capabilities into polished platform experiences.
Requirements
- Strong software engineering fundamentals and deep ML/AI knowledge.
- Production experience building systems involving LLMs, prompt engineering, structured outputs, RAG, or agent frameworks.
- Clear research experience, including top-tier conference publications, blogs, or reports.
- Strong experimental design skills and diligence in running experiments.
- Ability to manage complexity across multiple workstreams.
- Ability to read research papers and translate prototypes into shippable engineering.
- Experience with reinforcement learning, RLHF, or similar human-in-the-loop learning systems is beneficial.
- Background building evaluation frameworks, benchmarks, or data quality systems is beneficial.
- Experience with continual learning, memory systems, or knowledge distillation is beneficial.
- Understanding of multi-agent system architectures and their trade-offs is beneficial.
- Published AI/ML systems work or open-source contributions is beneficial.
- Previous founder or early-stage engineering experience is beneficial.
Benefits
- Competitive compensation and equity
- Generous health benefits
- Unlimited PTO
- Paid parental leave
- Daily lunches and dinners
- Transportation and relocation support
- Retirement plans
- Visa sponsorship
- Based in San Francisco with work from the Mission office
Categories
About Applied Compute
Applied Compute builds enterprise AI systems that turn internal knowledge into custom models and deployable agent workforces. Its platform and forward-deployed teams create evals, train models on proprietary data, and run continually learning agents inside customer environments. The company is privately held and headquartered in San Francisco, serving enterprises that want in-house, domain-specific AI rather than generic foundation models.