Staff Machine Learning Engineer
London
Relay Technologies
UK’s new parcel shipping solution built purposefully for online delivery. Transform your next-day delivery experience with our zero-waste supply chain.Relay is a warehouse-to-doorstep delivery network purpose built to scale and adapt to the demands of e-commerce. We help the most important e-commerce retailers in the UK (and one day, the world) to deliver faster, more affordably, and with a smaller carbon footprint than existing solutions.
Our success depends on our ability to deliver parcels efficiently (minimising delivery cost, energy expenditure, and greenhouse gas emissions), while maximising quality (minimising lost parcels and maximising on-time delivery). As we scale our network, we will increasingly rely on machine learning to drive these properties. Some typical use cases include…
Identifying couriers having a tough time on the road, so that our operations team can reach out and help them get back on track
Improving our route length predictions, so we can offer courier accurate market pay
Extracting features from proof-of-delivery photos and assessing the risk that a parcel delivered to a safe place will be lost or stolen before its recipient can take possession of it.
We’re looking for an exceptional machine learning engineer to join our engineering team. In this role, you can expect to…
Build our machine learning engineering discipline from the ground up, deploying models, engineering features, and standing up the infrastructure that makes it all possible
Take ownership over mission-critical components in the “brain” of our logistics network
Work closely with fellow technologists (data scientists, backend and mobile engineers) as well as with members of our operations teams
Regularly spend time in the field learning how the technology you build impacts our couriers and parcel recipients.
You might be a great fit for this role if…
You have deep prior experience with machine learning, but don’t just think of yourself as a “machine learning engineer”
You are excited to take on a wide range of challenges within machine learning and software engineering
You are practical and impact-oriented. You are scientific and rigorous in your work, but you can’t stand “science projects” – technology built for its own sake rather than to benefit end users or the business
You act with agency and take pride of ownership in your work. You naturally take initiative, seeking out the best opportunities for impact.
You have deep empathy for the humans for whom you build technology, including customers, partners, and your fellow colleagues. You seek out the chance to hear directly from them and go out of your way to incorporate their feedback into your work.
You are eager to learn new technologies and take on new problem domains.
You value and practise clear communication, active listening, and intentional collaboration.
We are looking for candidates who…
Have at least two years of experience deploying models in production and working on machine learning infrastructure
Have worked on high-performing teams building software for at least four years.
Have broad experience across a variety of technology stacks.
What we offer:
25 days annual leave per year (plus bank holidays).
Generous equity package.
Bupa Global: Business Premier Health Plan - Comprehensive global health insurance with direct access to specialists, dental care, mental health support and more.
Contributory pension scheme.
Hybrid working in our Dog-friendly co-working space; we're based in London near Old Street tube station.
Free membership of the gym in our co-working space in London.
Cycle-to-work scheme.
A culture of learning and growth, where you're encouraged to take ownership from day one.
Plenty of team socials and events - from pottery painting to life-size Monopoly and escape rooms.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: E-commerce Engineering Machine Learning ML infrastructure
Perks/benefits: Career development Equity / stock options Fitness / gym Health care Team events
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