Founding Applied Scientist

Role overview Shape applied science for production AI systems that learn from experience and improve measurable business outcomes. You’ll lead research and engineering work across agent-driven improvement projects, translating ideas in areas such as recommendation, search, pricing, risk, forecasting, and marketing science into systems that operate on real data.

Responsibilities - Build and improve end-to-end learning systems using company code, data, and decision context. - Lead applied research projects with agent teams and evaluate their impact on business metrics. - Develop approaches for agent reasoning, memory, planning, and reinforcement learning. - Turn research into production systems, balancing model quality, serving latency, and engineering speed. - Set technical direction and architectural standards for the applied science work.

Requirements - Strong applied machine learning and engineering experience, including a record of shipping production systems. - Ability to design technical roadmaps and make sound architecture and model trade-offs. - Experience connecting research methods to real-world product or business outcomes. - Comfort working across research and engineering in a small, senior team.

Nice to have - A graduate degree in computer science, statistics, or a related quantitative field. - Publications at leading machine learning venues or experience at an advanced AI lab or growth-stage AI startup. - Background in recommendations, personalization, causal inference, or computational advertising.

Benefits and work setup - Full-time role, based in London or remote. - Founding equity and the opportunity to work on agentic reasoning, memory, and reinforcement learning with experienced researchers and engineers.