Applied Scientist: Scalable Recommender & Optimization

Relay Technologies; Greater London; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

Relay Technologies in London is seeking an Applied Scientist to advance the company's recommender systems and optimization efforts. You will drive the transition from research to production, designing models that influence routing, pricing, and logistics decisions across our platform.

The role requires deep knowledge of recommender systems, reinforcement learning, and large language models, with a focus on experimentation and scalable impact across a fast-growing, data-driven environment.

Qualifications

• Experience building recommender systems at scale and taking them to production.
• Strong background in ML, optimization, and data-driven decision making.
• Familiarity with reinforcement learning, causal inference, and AI methods.
• Ability to design experiments, validate hypotheses, and iterate quickly.
• Excellent collaboration and clear communication with cross-functional teams.

Responsibilities

• Set the standard for Relay's recommender systems across products.
• Lead the evolution of Relay's recommender from research to production.
• Design and build the recommender systems powering key decisions.
• Partner with Operational Research to align recommender models with optimization.
• Contribute to Relay's research culture and technical community.

Skills

Recommender systems

Reinforcement learning

LLMs

Productionising ML models

Experimentation

Job description

Relay Technologies in London is seeking an Applied Scientist to advance the company's recommender systems and optimization efforts. You will drive the transition from research to production, designing models that influence routing, pricing, and logistics decisions across our platform.

The role requires deep knowledge of recommender systems, reinforcement learning, and large language models, with a focus on experimentation and scalable impact across a fast-growing, data-driven environment.