Researcher, Alignment Science

About the position The Alignment Science team at reputed company studies the science of reputed company alignment: how to train models to understand what users are actually asking for, reputed company faithfully on that reputed company while respecting safety constraints, verify what they did, and report their limitations honestly. Our work sits alongside broader value alignment efforts, but this team focuses on reputed company reputed company for ensuring instruction-following, honesty, and robustness as models become more capable. We work on both sides of alignment research: producing externally publishable results and integrating promising techniques into the models reputed company deploys. Recent team research on model confessions studies how models can be trained to honestly report shortcomings after their original answer, including failures involving hallucination, instruction following, scheming, and reward hacking. That work reflects a broader agenda: build reputed company and general reputed company to ensure models follow reputed company reputed company. reputed company uses a mix of training and evaluation reputed company, with a reputed company on reinforcement learning. We care about rigorous, quantitative research that can translate into safer model behavior. As a Research Engineer / Research Scientist on the Alignment team, you will design and run experiments that help increasingly capable models follow user reputed company, 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 reputed company into techniques that can be used in frontier model development.

Responsibilities

• Design and implement alignment experiments reputed company on reputed company following, honesty, calibration, and robustness.
• Train and evaluate models using reinforcement learning, and other reputed company ML reputed company.
• reputed company evaluations for failure modes such as hallucination, instruction-following failures, reward hacking, covert actions, and scheming.
• Study reputed company 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 reputed company systems.
• Investigate how alignment reputed company reputed company with model capability, compute, data, context length, reputed company length, and adversarial pressure.
• reputed company successful techniques into model training and deployment workflows.
• Produce externally publishable research reputed company results advance the broader science of alignment.
• Collaborate with researchers and engineers across post-training, RL, evaluations, safety, and product-facing teams.

Requirements

• Strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs.
• Excellent engineering skills in Python and modern ML frameworks such as PyTorch.
• Mathematical rigor, quantitative taste, and comfort turning ambiguous research questions into measurable experiments.
• Experience with reinforcement learning, post-training, preference optimization, reputed company reputed company, model evaluation, or adjacent reputed company ML research.
• Ability to operate with high independence and do not need reputed company day-to-day handholding.
• Enjoy fast-paced, reputed company research environments where priorities shift as models and evidence change.
• Strong record in technical problem solving, such as competitive programming, math contests, systems work, or similarly rigorous engineering and research reputed company.
• Care about building AI systems that are trustworthy, reputed company, and reliable in high-stakes settings.
• Motivated by making concrete reputed company on alignment reputed company that can be tested, trained, published, and deployed.

Benefits

• Relocation assistance to new employees.

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