Member of Technical Staff, Evals

About the role

Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pretraining, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.

About the Role

The Evals team measures the performance and capabilities of internal and external models, helping teams across Magic make informed decisions. The team supports pretraining, RL, data, inference, and product, and sits on the critical path of many of the company's most important decisions.

As a Member of Technical Staff on Evals, you will develop new benchmarks and evaluation methodologies, run data ablations, assess dataset quality, and investigate whether our measurements accurately reflect model performance.

Sweating the details matters on this team. Many benchmarks, papers, and open-source evaluation frameworks contain subtle bugs or flawed assumptions that lead to misleading conclusions. We care deeply about correctness, reproducibility, and measurement quality.

What You'll Work on

• Design and validate new evaluations and benchmarks across model training and deployment
• Investigate model behavior and identify gaps in existing evaluations
• Measure dataset quality and identify opportunities to improve training data
• Improve evaluation correctness, reproducibility, and reliability
• Audit and improve upon public benchmarks, research papers, and open-source implementations
• Define metrics that accurately reflect model quality
• Build tools that make evaluation results accessible and useful across Magic

What We're Looking for

• Experience developing model evaluations, benchmarks, or ML experiments
• Strong programming skills and experience working with machine learning systems
• Track record of owning technical projects end-to-end
• Skepticism toward results that cannot be reproduced, validated, or explained
• Experience analyzing model performance and interpreting experimental results
• Comfortable navigating ambiguity and determining whether a measurement actually captures the behavior it claims to measure
• Commitment to raising the standard of rigor in AI evaluations

Compensation, Benefits, and Perks (Us)

• Annual salary range between $200K - $550K depending on experience
• Equity is a significant part of total compensation, in addition to salary
• 401(k) plan with 6% salary matching
• Generous health, dental, and vision insurance for you and your dependents
• Unlimited paid time off
• Visa sponsorship and relocation support for candidates moving to San Francisco
• A small, fast-moving, highly collaborative team working on frontier AI systems