Member of Technical Staff - Software Engineering Capabilities
About the role
About Us Preference Model is building automated ML research engineering. Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.
About
the Role AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering: working in a large codebase with real conventions and technical debt, making the right tradeoff on a system design problem, or navigating a multi-step task with ambiguous stakeholders. As a Member of Technical Staff - Software Engineer on the Capabilities team , you will build software engineering environments that expose those failures and help models improve on them. This role blends research and engineering . It will require you to stay up to date with the latest research, develop novel approaches, and realize them in code. You will have full ownership and autonomy of the environments you build. You will own our most complex tasks end-to-end: environments with multi-step workflows, realistic stakeholder interactions, large codebases, and challenging system design problems. You will work closely with a small team of engineers and directly with our founders, and you will ship environments that go into the training loops of frontier models at our partner labs. This is independent, high-ownership work with regular feedback. What You Will Do Design, build, and refine RL tasks across their full lifecycle, from ideation through grading, failure analysis, and iteration. Own the hardest environments on the roadmap: multi-step workflows, realistic stakeholder interactions, large codebases with real conventions and technical debt, and system design problems. Direct coding agents heavily in your day-to-day work, evaluate their output critically, and recognize when they are failing in subtle ways. Distinguish genuine model capability gaps from grader or environment issues, and redesign tasks to target deeper, more subtle failure modes. Contribute to the shared infrastructure and tooling that the environments team depends on. Mentor newer engineers on
