LLM Safety Research Engineer

Capitolis; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Dynamo AI in San Francisco seeks a research‑driven ML engineer focused on LLM safety to own a dedicated vertical, exploring a specific safety domain or use case from defense or red‑team perspectives.

You will generate high‑quality synthetic data, train LLMs, benchmark rigorously, and deliver robust production code, while co‑authoring papers and presentations with the research team to advance safe and responsible AI adoption.

Qualifications

• Deep domain knowledge in LLM safety techniques.
• Extensive experience in designing, training, and implementing multiple different types of LLM models and architectures in the real world. Comfortability with leading end‑to‑end projects.
• Adaptability and flexibility. In both the academic and startup world, a new finding in the community may necessitate an abrupt shift in focus. You must be able to learn, implement, and extend state‑of‑the-art research.
• Preferred: past research or projects in either attacking or defending LLMs.

Responsibilities

• Own an LLM vertical with a focus on a specific safety domain, technique, or use case (either from defense or red‑team attack perspective)
• Generate high quality synthetic data, train LLMs, and conduct rigorous benchmarking.
• Deliver robust, scalable, and reproducible production code.
• Push the envelope by developing novel techniques and research that delivers the world’s most harmless and helpful models. Your research will directly empower our customers to more feasibly deploy safe and responsible LLMs.
• Co-author papers, patents, and presentations with our research team by integrating other members’ work with your vertical.

Skills

LLM safety

End-to-end projects

Research experience

Adaptability

Job description

Dynamo AI in San Francisco seeks a research‑driven ML engineer focused on LLM safety to own a dedicated vertical, exploring a specific safety domain or use case from defense or red‑team perspectives.

You will generate high‑quality synthetic data, train LLMs, benchmark rigorously, and deliver robust production code, while co‑authoring papers and presentations with the research team to advance safe and responsible AI adoption.