Location & Travel Expectations
We are primarily seeking candidates based in the Washington, DC, New York City, San Francisco Bay Area, and Denver metropolitan areas. Candidates hired in these locations will be expected to work from the office at least two days per week.
All new hires will participate in in-person onboarding in Washington, DC, New York City, or San Francisco during their first week. Occasional travel may also be required for interviews, in-person meetings, and other business needs.
About the Role
We are seeking a Machine Learning Engineer to design, build, and evaluate advanced machine learning systems across AI safety and model evaluation applications.
This role combines strong ML engineering with an experimental mindset. You will work on problems involving reinforcement learning, model evaluations, language models, multimodal systems, and classifiers, taking ambiguous technical questions and turning them into rigorous experiments and scalable systems.
You will collaborate closely with engineers, analysts, red teamers, and subject-matter experts supporting leading AI organizations.
What You'll Do
- Design and run ML experiments to evaluate the capabilities, behavior, robustness, and limitations of advanced AI systems.
- Develop and evaluate models across reinforcement learning, NLP/LLMs, computer vision, and multimodal ML.
- Build evaluation pipelines, benchmarks, datasets, and metrics for frontier AI systems.
- Train, fine-tune, and evaluate models for safety, security, and other high-impact applications.
- Develop reliable tooling and infrastructure to run ML experiments and evaluations at scale.
- Analyze results, identify model failure modes, and translate findings into new experiments and technical approaches.
What We're Looking For
- 3–5+ years of experience in machine learning, research engineering, or a related technical field.
- Strong Python skills and experience with ML frameworks such as PyTorch or JAX.
- Hands-on experience training, fine-tuning, or evaluating modern ML models.
- Strong understanding of experimental design, model evaluation, and quantitative analysis.
- Familiarity with agentic AI fundamentals, including common harnesses, Model Context Protocol, agent benchmarks, and security risks to AI agents.
- Experience in one or more of the following: reinforcement learning, NLP/LLMs, computer vision, or multimodal ML.
- Strong software engineering fundamentals and the ability to work independently on ambiguous technical problems.
Nice to Have
- Experience with RLHF/RLAIF, reward modeling, policy optimization, or other model post-training techniques.
- Experience evaluating frontier language or multimodal models.
- Experience with adversarial evaluations, robustness testing, or AI safety.
- Experience with distributed training, cloud ML infrastructure, or large-scale ML systems.