Senior Machine Learning Engineer

About this role

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 parcel Relay handles is touched by ML.

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. That's 10+ models running in the critical path of a live logistics network where quality is non-negotiable. ML Stack Highlights Python and Rust. We keep things simple but use the right tool for the job Rust with ONNX in-process model execution where throughput is critical Chalk.ai as our Feature Store GCP Agent Platform Endpoints for model serving Cloud-native on GCP.

Services run on Kubernetes, with extensive use of BigQuery The Opportunity As a Senior Machine Learning Engineer at Relay, you'll: Own critical part of ML: productionising of 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 adoption and set the standards for how Relay ships ML org-wide.