Job Description

Machine Learning Engineer - Post Training

A stealth-stage venture backed by Lux Capital (investors in DeepMind and OpenAI) is developing frontier-scale AI systems for high-impact applications in human health and decision-making. The team is applying LLMs and multimodal agents to complex real-world problems where precision, safety, and interpretability matter.

This role focuses on post-training workflows for large models, including RLHF, reward modeling, and evaluation. Ideal candidates have experience aligning agent behavior with nuanced goals in high-stakes environments. The position involves close collaboration with research and product teams to define evaluation criteria, build scalable RL pipelines, and ship production-grade systems.

Ideal Experience:

• Designing and scaling RL environments for LLMs
• Building high-quality evaluation pipelines for frontier models
• Collaborating with domain experts to define evaluation tools and metrics
• Crafting training datasets and reward functions using LLMs and/or human feedback
• Training large models with RLHF, DPO, or instruction tuning
• Translating abstract requirements into concrete evaluation frameworks and agent behaviors

Bonus:

• End-to-end experience shipping ML systems in production
• Prior startup or zero-to-one experience
• Experience with PyTorch, JAX, or other modern ML frameworks
• Familiarity with multi-cloud infrastructure and distributed compute
• While familiarity with biomedical data is welcome, candidates from non-bio backgrounds are strongly encouraged to apply. The team values engineering excellence and creativity over domain-specific experience.

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