Research Engineer, Post-Training
San Francisco
Job description
Collision AI is building frontier cybersecurity models to help defend our customers. We’re looking for a researcher to develop the training methods and infrastructure behind them, with a focus on post-training and continual learning from real security work.
You’ll take research ideas from an initial hypothesis to deployed models. That means identifying useful signals in production telemetry, building training datasets, testing new algorithms, and developing evaluations that measure how well our models and agents perform. You’ll own the full experimental cycle, combining research with hands-on LLM training and serving.
As an early member of the team, you’ll work directly with the founders and design partners to decide what we build and how. Your work will shape the technical foundations, product direction, and the way we conduct research as a company.
Required qualifications
- Experience training large language models.
- Proficiency in PyTorch, JAX, or a comparable ML framework, with the engineering skills to turn research ideas into working systems.
- Strong experimental judgment: forming testable hypotheses, choosing meaningful measurements, and using results to guide the next experiment.
Bonus qualifications
- Experience with distributed training, high-performance computing, or debugging experiments across large compute clusters.
- Contributions to open-source ML research, training systems, or evaluation infrastructure.
- Evidence of independent technical thinking through research, open-source work, or ambitious personal projects.
- A master’s degree or relevant research experience.
How to apply
To apply, email hello@collision.ai