Applied AI Research Scientist, Continual Learning (Physical-World AI)

An AI startup building software agents that perform engineering work in the physical world is hiring a research scientist to lead how those agents keep learning after deployment. This is an individual contributor position at principal level, and the results go directly to customers rather than staying in a lab.

Your focus

Define from scratch the research agenda covering memory formation, self-improvement, rapid adaptation and continual learning

Steer the technical approach at principal level within a small group of researchers and engineers

Take promising ideas from the roadmap, sharpen them and start experiments right away

Carry research into production alongside AI and software engineers, covering data creation, evaluation and folding results into the product

Chase breakthroughs that make the agents more capable, while publishing strong results

About you

A doctorate or comparable experience in computer science, robotics, engineering, mathematics, physics or a neighboring discipline

Roughly three to seven years of research that has been put to use in practice, with publications at leading AI and ML venues

Deep background spanning meta-learning, lifelong learning, systems that improve themselves, memory mechanisms and adapting models; agentic work over long horizons is a major advantage

Practical experience with post-training, including supervised fine-tuning and reinforcement learning

Strong command of Python, JAX or PyTorch and large-scale distributed training

Motivated by a scrappy early-stage setting where you own outcomes

On-site in the San Francisco Bay Area. Not able to offer visa sponsorship or transfers.