Research Engineer, Safety & Alignment

Role Mission

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.

What You'll Accomplish

• Build agentic safety evals: Design and run evaluations for the failure modes that matter for autonomous agents: unsafe actions, prompt injection, data exfiltration, sandbox escape, reward hacking, and misuse. Make them fast enough to run on every model and product release.
• Red-team Devin: Attack the agent and its harness systematically. Find the 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: 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.

Exceptional Candidates Have Demonstrated

• Safety or alignment research experience: Hands-on work on evaluations, red-teaming, alignment, or interpretability at a frontier AI lab or in published research. You have built things that changed how a model was trained or deployed.
• Agentic systems understanding: You know how agents fail in practice: tool misuse, specification gaming, long-horizon drift, adversarial inputs. You have measured it or have concrete ideas about how to.
• Strong engineering fundamentals: Proficiency in Python and PyTorch (or JAX). You can build eval infrastructure, run experiments at scale, and read the training and inference code.
• Empirical rigor: You design clean experiments, report results honestly, and know the difference between a real improvement and noise.
• Comfort with adversarial thinking: You enjoy breaking systems and are good at it.
• Relevant industry experience: Prior experience at a frontier AI lab, applied AI company, or developer tools company.
• Advanced degree: PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline; or equivalent industry research experience.