About This Role
You will work at the intersection of research and applied engineering, exploring how modern ML — including LLMs, reinforcement learning, and structured prediction — can power autonomous decisioning in high-stakes domains. You will own the research roadmap for one or more product surfaces, shape the experimental infrastructure, and partner with engineering to ship.
What you will do
- Design and run rigorous experiments on candidate model architectures and decisioning algorithms.
- Translate findings into production-grade prototypes and partner with MLOps to deploy.
- Read, summarize, and reproduce relevant academic and industry research.
- Build evaluation harnesses, eval datasets, and metrics that reflect real customer outcomes.
- Publish internal research notes and contribute to external publications when appropriate.
What we are looking for
- PhD or equivalent industry experience in ML, statistics, or a related quantitative field.
- Strong publication record or evidence of shipped applied-research work.
- Fluent in PyTorch or JAX; comfortable with distributed training.
- Strong software engineering fundamentals — you can take a notebook to a service.
- Clear technical writing and a habit of de-risking ideas with simple experiments first.
Nice to have
- Experience with autonomous decisioning, Bayesian inference, or causal inference.
- Hands-on experience deploying LLM-powered agents or tool-use systems.
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