Machine Learning Engineer

10a Labs provides safety and threat-intelligence services for frontier AI labs and other technology organizations. The company is seeking a Machine Learning Engineer to design, build, and evaluate machine learning systems for AI safety and model evaluation, including experiments, evaluation pipelines, models, datasets, metrics, and scalable infrastructure.

Responsibilities:

• Design and run ML experiments to evaluate the capabilities, behavior, robustness, and limitations of advanced AI systems
• Develop and evaluate models acrossreinforcement 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:

• 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