Applied Researcher - Continual Learning

Highlights

• Build continual learning systems turning agent trajectories into model updates.
• Compare streaming RL, batched post-training and distillation for production agents.
• Own promotion gates and oversight for agents that keep changing live.

What they offer

• Equity incentives
• Flexible work across San Francisco, New York, London or remote
• Visa sponsorship and relocation support
• Professional development budget
• Team off-sites and conference attendance

Ideal candidate

Experimental builder who thrives in ambiguity and loves turning messy production behavior into measurable learning. Excited to work directly with collaborators and ship open research that improves live agents.

Requirements

• Experience in continual or online learning, RL, meta-learning, memory systems or adaptive production ML
• Hands-on experience building and debugging training, post-training, agent or evaluation loops
• Strong experimental judgment with meaningful baselines and clear failure attribution
• Strong Python and ability to build reusable research artifacts
• Comfort owning ambiguous problems and working directly with external partners