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Greater London, England, United Kingdom
Hybrid
Relay is fundamentally reshaping how goods move in an online era. Backed by Europe’s largest-ever logistics Series A ($35M) led by deep-tech investors Plural, and a recently closed Series B ($95M), Relay is scaling faster than 99% of venture-backed startups. We're assembling the most talent-dense team the logistics industry has ever seen
Relay’s Mission is to free commerce from friction. Today, high delivery costs act as a hidden tax on e-commerce, quietly shaping what can be sold online and limiting who can participate. We envision a world where more goods move more freely between more people, making the online shopping experience seamless and accessible to everyone.
THE TEAM
• ~160 people, around half in engineering, product and data
• 45+ advanced degrees across computer science, mathematics and operations research
• Thousands of data points captured, calculated, analysed and predicted for every single parcel we handle
• An intellectually vibrant culture of first‑principles thinking, tight feedback loops and relentless experimentation
Every courier who works with Relay chooses their day. A day is a set of routes that have to fit together, and every courier's ideal day competes with everyone else's. Work is finite and every route still has to be covered, so matching thousands of couriers to the work they want is a recommendation problem with few precedents.
Science at Relay is the brain of the operation: machine learning, operational research, recommender systems, causal inference, location intelligence and experimentation, working together to decide how every parcel gets delivered. There's no existing recommender to iterate on. You'll build it.
Combinatorial, constrained and competitive: every recommendation has to be feasible for the whole network, not just right for one courier.
Your models power large-scale optimisation that balances thousands of couriers' preferences against the constraints of a physical network.
Price is part of the recommendation, so economics, personalisation and optimisation have to work as one.
Our apps collect millions of data points every day.
As a Principal Machine Learning Scientist at Relay, you'll:
Set the standard for recommender systems at Relay.
Lead the evolution of Relay's recommender from research to production.
Design and build the recommender systems that power Relay's key decisions.
Partner with Operational Research to unite recommender models and optimisation.
Contribute to Relay's research culture, and develop our voice in the tech community.
Have built recommender systems at scale, owned them in production, and led their direction.
Bring deep expertise in recommender systems, reinforcement learning and LLMs, whether from a PhD, publications, or years at the frontier in industry.
Think in experiments, and let evidence guide decisions.
Are energised by open-ended problems, and know how to make them tractable.
Collaborate generously, and explain complex ideas simply.
Don't tick every box? If this problem excites you, we'd still love to hear from you.
Who Thrives at Relay?
Aim with Precision: You define problems clearly and measure your impact meticulously.
Play to Win: You chase bold bets, tackle the hard stuff, and view constraints as fuel, not friction.
1% Better Every Day: You believe that small, consistent improvements lead to exponential growth. You move quickly, deliver results, and learn from every experience.
All In, All the Time: You show up and step up. You take ownership from start to finish and do what it takes to deliver when it counts.
People-Powered Greatness: You invest in your teammates. You give and receive feedback with care and candour. You build trust through high standards and shared success.
Grow the Whole Pie: You seek out win-win solutions for merchants, couriers, and our customers, because when they thrive, so do we.
If these resonate, and you combine strong technical fundamentals with entrepreneurial drive, let’s connect.
Relay is an equal-opportunity employer committed to diversity, inclusion, and fostering a workplace where everyone thrives.