At a Glance
- Tasks: Lead the production of machine learning models and enhance our ML platform.
- Company: Join Relay, a fast-growing logistics startup backed by major investors.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Make a real impact on e-commerce by optimising delivery processes.
- Qualifications: Experience in deploying ML models and strong software development skills required.
The predicted salary is between 60000 - 80000 £ per year.
About Relay
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 (whose portfolio spans fusion energy and space exploration), Relay is scaling faster than 99.98% of venture‑backed startups.
Our mission is to free commerce from friction.
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
- ~110 people, more than 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
- Technology Stack
Every parcel Relay handles is touched by machine learning.
We recommend and optimise route assignment, predict delivery durations, estimate parcel dimensions and weight, detect objects in images on device, forecast demand and decide network handovers.
More than ten models run in the critical path of our live logistics network where quality is non‑negotiable.
Our stack includes Python and Rust; Rust with ONNX in‑process model execution for throughput‑critical services; Chalk. ai as our feature store; GCP Agent Platform Endpoints for model serving; and a cloud‑native architecture on GCP, with services running on Kubernetes and extensive use of Big Query.
Role Overview
As a Senior Machine Learning Engineer at Relay, you will own the critical part of ML: productionising our models end‑to‑end, from training pipeline through live integration to measured business impact.
- Build and mature our ML platform, evolve the model serving architecture, expand reusable components and set standards for how Relay ships ML organization‑wide.
- Strengthen existing models by architecting integration and system testing within training pipelines, automating releases, and monitoring drift.
- Launch completely revamped processes side‑by‑side with data scientists and measure their real‑world impact on the network.
Qualifications
- At least two years deploying and operating models in production and four years building software on high‑performing teams.
- Comfortable diving into unfamiliar codebases and languages to ship a model into a live system.
- Prefer building automation that removes manual labour over repeating it.
- Culture & Values
- Aim with precision: define problems clearly and measure your impact meticulously.
- Play to win: chase bold bets, tackle the hard stuff, view constraints as fuel, not friction.
- 1% better every day: small, consistent improvements lead to exponential growth.
- All in, all the time: take ownership from start to finish and deliver when it counts.
- People‑powered greatness: invest in teammates, give and receive feedback with care and candour, build trust through high standards and shared success.
- Grow the whole pie: seek win‑win solutions for merchants, couriers, and customers.
- Equal Opportunity Statement
Relay is an equal‑opportunity employer committed to diversity, inclusion, and fostering a workplace where everyone thrives.
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Senior Machine Learning Engineer employer: Doist
Brink's is an exceptional employer that fosters a collaborative and innovative work culture, empowering employees to drive global commercial strategies in the ATM lifecycle solutions sector. With a strong focus on professional development and growth opportunities, employees are encouraged to expand their skills while contributing to sustainable growth and operational excellence. Located in a dynamic environment, Brink's offers unique advantages such as a diverse team and the chance to build long-term partnerships with global customers, making it a rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer
✨Join Local Tech Meetups
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✨Contribute to Open Source Projects
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We think you need these skills to ace Senior Machine Learning Engineer
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Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Doist.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Doist and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
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How to prepare for a job interview at Doist
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
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Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Doist uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
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Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
