About the job
As a Research Engineer / Research Scientist on the Alignment team, you will design and run experiments that help increasingly capable models follow user intent, remain calibrated about correctness and risk, and honestly surface their own mistakes. You will work on hands-on model training, evaluation design, and research infrastructure, while helping turn promising alignment methods into techniques that can be used in frontier model development.
Responsibilities
Design and implement alignment experiments focused on intent following, honesty, calibration, and robustness.
Train and evaluate models using reinforcement learning, and other empirical ML methods.
Develop evaluations for failure modes such as hallucination, instruction-following failures, reward hacking, covert actions, and scheming.
Study methods that encourage models to verify their behavior and report shortcomings honestly, including confession-style training objectives.
Build monitoring and inference-time interventions that ensure compliant behavior or surface model issues to users or downstream systems.
Investigate how alignment methods scale with model capability, compute, data, context length, action length, and adversarial pressure.
Integrate successful techniques into model training and deployment workflows.
Produce externally publishable research when results advance the broader science of alignment.
Collaborate with researchers and engineers across post-training, RL, evaluations, safety, and product-facing teams.
Qualifications
Minimum
No minimum qualifications listed.
Preferred
Have strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs.
Have excellent engineering skills in Python and modern ML frameworks such as PyTorch.
Bring mathematical rigor, quantitative taste, and comfort turning ambiguous research questions into measurable experiments.
Have experience with reinforcement learning, post-training, preference optimization, scalable oversight, model evaluation, or adjacent empirical ML research.
Can operate with high independence and do not need close day-to-day handholding.
Enjoy fast-paced, collaborative research environments where priorities shift as models and evidence change.
Have a strong record in technical problem solving, such as competitive programming, math contests, systems work, or similarly rigorous engineering and research projects.
Care about building AI systems that are trustworthy, honest, and reliable in high-stakes settings.
Are motivated by making concrete progress on alignment methods that can be tested, trained, published, and deployed.