Machine Learning Engineer

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.

• →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.

• 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.

• 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.

We don't expect candidates to have experience across every area above. We value deep ML expertise, strong experimental instincts, and the ability to quickly learn new techniques.