It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. Backed by Greylock and Sequoia.
About the role
As a Staff Research Scientist, you will independently lead a major workstream in agent learning and recursive self-improvement. You will turn systematic failures and successful trajectories into hypotheses, experiments, training signals, and deployable improvements to model weights and/or the executable harness around the model. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 10+ years of relevant AI/ML research or engineering experience, or equivalent research depth and impact, PhD or other advanced degree required
- Track record of setting technical direction and leading multiple ambiguous, high-impact research efforts across team boundaries
- Strong foundations in machine learning, deep learning, reinforcement learning, and experimentation, with hands-on experience training or adapting large language or multimodal models
- Advanced Python and PyTorch skills, including modifying training code, data pipelines, evaluators, or research infrastructure
- Practical depth in agentic AI, including tool use, planning, memory, retrieval, environments, or long-horizon execution
- Experience designing decision-useful evaluations using robust datasets, trajectory analysis, graders or verifiers, and error analysis
More about this role
About the team
Our Core AI Research team develops novel methods for enterprise agents that reason over multimodal information, use tools, take reliable action across stateful workflows, and improve through feedback. We work across LLM model post-training, agent harnesses, training environments, evaluations, ML, search and reasoning systems, partnering closely with product, engineering, infrastructure, security, and domain experts.
About the role
As a Staff Research Scientist, you will independently lead a major workstream in agent learning and recursive self-improvement. You will turn systematic failures and successful trajectories into hypotheses, experiments, training signals, and deployable improvements to model weights and/or the executable harness around the model.
This is a research role for someone who can move between scientific reasoning, training code, agent systems, and production constraints.
- Design and execute end-to-end research projects that improve long-horizon enterprise agents across planning, reasoning, memory, tool use, retrieval, computer use, multi-agent coordination, and verification.
- Research model post-training methods such as continued pretraining,...
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