Machine Learning Engineer

About the role

This is a full-time Machine Learning Engineer position at a well-funded, stealth-stage AI startup based in Palo Alto, California. The team is building at the intersection of artificial intelligence and the life sciences, and is looking for engineers who want their work to reach production quickly and have tangible impact. As one of the earliest hires, you would have meaningful ownership from the start and work closely with the founding team.

Key responsibilities

• Develop, fine-tune, train, and assess large-scale models, including methods such as SFT, DPO/RLHF, and LoRA/PEFT, while managing the workflow from data preparation through deployment.
• Create and improve serving and inference systems that can support production-level speed and throughput.
• Build testing and evaluation frameworks that measure model quality against practical, real-world standards rather than only offline metrics.
• Work directly with founders and subject-matter experts to convert complex scientific challenges into machine learning solutions.
• Operate effectively in an early-stage 0-to-1 environment where processes are created rather than inherited.

Requirements

• Practical machine learning engineering experience, with a track record of training and shipping models rather than only using existing APIs.
• Strong programming ability in Python and solid experience with deep learning frameworks such as PyTorch or similar tools.
• Exposure to model training, fine-tuning, serving, optimization, and evaluation in a production context.
• A strong ownership mindset with comfort working through ambiguity and a bias toward delivering outcomes end to end.
• Open to applicants across the experience spectrum, from junior/mid-level candidates to senior/lead engineers.

Additional information

This role is available across the full range of 1 to 10 years of experience. Junior and mid-level candidates should bring strong fundamentals, some exposure to production ML, and a fast-learning mindset. Senior and lead candidates should have deep expertise in training and serving systems, along with the judgment to guide technical direction and raise team standards.

Nice to have

• Experience working with life sciences, biology, or scientific and research-oriented datasets.
• Hands-on knowledge of inference optimization or large-scale distributed training, including tools and techniques such as TensorRT, Triton, and quantization.
• Experience designing evaluation or benchmarking infrastructure from the ground up.

Why join

The company offers the chance to work with a founding team on a meaningful problem space, backed by strong investors and supported by frontier compute. The environment is collaborative and in-person in Palo Alto, with a preference for building together in the same room.