Jobs at Relay Technologies

As a Senior Software Engineer at Relay, you will tackle challenging problems, collaborate with talented teams, and have a direct impact on the company's growth through software development and optimization tools.

Build and own cross-platform mobile applications while contributing across the full technology stack, from mobile interfaces through data infrastructure. Solve complex logistics problems using technologies such as machine vision, machine learning, and LLM inference. Collaborate cross-functionally, take end-to-end ownership, and regularly engage with field operations to understand the impact on couriers and parcel recipients.

Leads an engineering team while remaining technically engaged through code reviews, pairing, and high-impact contributions. Sets direction, converts ambiguity into delivery plans, and partners with Product, Design, and Operations. Improves code quality, observability, incident response, analytics, and developer velocity. Owns hiring, coaching, feedback, performance management, and team growth in a fast-paced startup environment.

Build core API concepts, courier-facing delivery software, and data infrastructure supporting routing and operations research. The role spans cloud infrastructure, frontend development, machine learning, and emerging robotics or IoT integrations. Engineers will work directly with users in the field, learn rapidly, collaborate closely, and take ownership of impactful software projects. Candidates should have strong technical fundamentals, internship or academic engineering experience, and be able to work from the London office four days weekly starting Summer 2026.

Lead courier acquisition strategy to grow Relay's courier fleet by expanding into new markets, optimizing CPA and conversion rates, running A/B tests and landing page experiments, integrating paid and unpaid channels, and automating scalable marketing processes.

The Senior Data Analyst will enhance courier performance management and optimize handovers by modeling trade-offs, running experiments, and improving data analytics.

As a Senior Data Scientist, you will build and maintain production models for pricing decisions, define pricing dynamics, and measure strategy impact within a collaborative team.

As Lead Data Analyst, you will monitor and ensure the accuracy of forecasting models, diagnose forecasting errors, and communicate findings to stakeholders for operational improvement.

Own end-to-end productionisation of ML models, build and mature the ML platform and serving architecture, integrate system testing and monitoring into training pipelines, automate releases and drift detection, and measure real-world impact alongside data scientists.

Design, build, and productionise large-scale optimisation models and bespoke solvers for last-mile scheduling, assignment and pricing. Translate operational requirements into mathematical programming and heuristic solutions, benchmark and monitor performance, and work cross-functionally with engineering and analytics to deploy and evolve automated decision systems.

Manage client network performance by improving operational metrics across strategic accounts. Develop improvement plans, track progress, and communicate updates to clients.

The Senior Data Analyst will own analytics for finance, create dashboards, ensure data quality, and communicate insights to support decision-making.

The Senior Data Scientist will build and maintain models for simulating the logistics network, addressing operational queries from finance teams about costs and efficiencies.

Own and build the integrated demand-forecasting models (D0–D30) that drive sorting, routing and network planning. Lead the hardest forecasting problems: long-horizon model generation, inbound international volume, parcel size/weight prediction. Define methodology, validation standards, model interfaces, and production quality while collaborating with ML engineers, analysts and operations to measure model impact on cost per parcel.

As a Senior Analytics Engineer, you'll design core data models, build AI system foundations, and ensure data quality while collaborating across teams.

Embed in the Pitstop squad to build monitoring and analytics for launch readiness, handover speed/accuracy, retention, and cost-per-parcel. Investigate performance, quantify initiative impact, diagnose churn and LiveOps ticket drivers, maintain dbt models and pipelines, and deliver KPIs and dashboards to guide squad decisions.