Platform Research Engineer

The Role

As a Platform Research Engineer at Applied Compute, you'll drive research to make our platform intelligent. You'll work on continual learning from production traces (OPSD, SDPO, RMSD), scaling synthetic environments, data and evaluations, and integrating the RL stack that powers agent improvement over time. Your research bridges AI product engineers—who build customer interfaces and tools—and applied research engineers who deploy agents to production. You'll identify common patterns across customer deployments and pursue research to accelerate delivery.

What You'll Do

• Build and improve hinting primitives for online self-distillation, enabling 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 multi-agent system design approaches, structuring agent coordination and managing trade-offs
• Build best-in-class coding agents for automated agent creation and hill-climbing harnesses
• Translate learnings from applied research and customer deployments into generalizable platform features
• Collaborate with AI product engineers to integrate new ML capabilities into polished platform experiences

What We're Looking For

• Strong software engineering fundamentals combined with deep ML/AI knowledge
• Production research experience: both model deployments to production and top-tier conference publications, blogs, or reports
• Strong experimental design skills with meticulous attention to detail and rigorous testing methodology
• Highly organized approach to managing 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 building evaluation frameworks, benchmarks, or data quality systems
• Experience with continual learning and distillation
• Informed perspectives on multi-agent system architectures and trade-offs
• Published work or open-source contributions in AI/ML systems
• Previous experience as a founder or early engineer at a zero-to-one company