Summary
What you’ll impact
The Machine Learning Engineer at the organization will design, build, and evaluate advanced machine learning systems for AI safety and model evaluation applications. The role involves developing experiments, models, pipelines, and tooling across reinforcement learning, NLP, computer vision, and multimodal domains while collaborating with engineers, analysts, red teamers, and subject‑matter experts.
Responsibilities
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.
Requirements
What you’ll bring
- 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.