About the Role

We're building continuous‑learning agents driven by our Learning Engine, a modular framework that lets agents learn from human knowledge bases, feedback, and through self‑discovery, then apply that learning—evolving into expert teammates that get better every day rather than demo‑stage agents. You'll partner directly with the founders to design, implement, and evaluate new algorithms that let agents learn continuously. Successful prototypes move quickly into production, so your research will have an immediate impact on real users.

Responsibilities

• Design and run state-of-the-art machine-learning experiments to extend our agentic Learning Engine
• Publish results through papers, datasets, and open-source code
• Collaborate with product and engineering to translate your research findings into the Learning Engine

Qualifications

• PhD in AI, Computer Science, or a related field — or an equivalent record of research excellence
• Publications at top AI conferences (NeurIPS, ICML, ACL, …) or an open-source project with significant traction
• Strong Python skills and experience with ML frameworks such as PyTorch
• Exceptional problem-solving ability; comfortable working independently and in teams
• Familiarity with graph databases, vector stores, or Elasticsearch for structured-data retrieval
• Knowledge of advanced training algorithms (e.g., DPO, RLHF, GRPO)
• Clear written and verbal communication

What We Offer

• Remote-friendly setup — work from anywhere in compatible time zones, with our office based in London
• Competitive, top-of-market compensation package
• Freedom to pursue ambitious research aligned with our mission
• Close collaboration with the founders and senior researchers
• Limitless cloud compute — GPU clusters and a war chest of cloud credits ready for your experiments
• Conference travel, publication, and open-source support
• Option to extend to 6 months or transition to a full-time role

Ready to Apply?

If you're excited about creating AI that learns continuously and autonomously, we'd love to hear from you.