Research Engineer, Safety & Alignment

Devin writes and runs code, uses tools, takes actions in real customer systems, and operates for hours without a human in the loop. Safety here is not a research paper; it is whether an agent behaves correctly in production, millions of times a day. You will be a founding member of Cognition's safety team, reporting to the Head of Safety. You will build the evaluations and red-teaming that gate every release, develop alignment methods that shape how our models are trained, and work directly with the agent team on how Devin plans, acts, and asks for help. This is a hands-on research engineering role for someone who wants their safety work to run in the loop of a real agent, not sit in a benchmark.

Responsibilities

Build agentic safety evals: Design and run evaluations for failure modes such as unsafe actions, prompt injection, data exfiltration, sandbox escape, reward hacking, and misuse, fast enough to run on every model and product release; Red-team Devin: Attack the agent and its harness systematically, find failures before customers do, and turn them into fixes and regression tests; Develop alignment methods: Work with post-training on reward modeling, preference data, constitutional approaches, and other techniques that make the model safer without making it worse at the job; Shape the agent harness: Partner with the agent team on permissions, oversight, and escalation, including when Devin should act, when it should ask, and how it should explain itself; Publish and share: Contribute to Cognition's external safety work through papers, evals, and open methods where it makes sense

Requirements

Safety or alignment research experience: hands-on work on evaluations, red-teaming, alignment, or interpretability at a frontier AI lab or in published research; Agentic systems understanding: knowledge of how agents fail in practice, such as tool misuse, specification gaming, long-horizon drift, and adversarial inputs; Strong engineering fundamentals: proficiency in Python and PyTorch (or JAX); Empirical rigor: designing clean experiments and reporting results honestly; Comfort with adversarial thinking; Relevant industry experience at a frontier AI lab, applied AI company, or developer tools company; Advanced degree in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline, or equivalent industry research experience