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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