Forward Deployed Engineer
Relay
| Company | Relay |
| Category | Engineering |
| Location | London |
| Remote | On-site |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 7 Apr 2026 |
| Last verified | 31 Jul 2026 |
| Source | Employer career page (ashby) |
Description
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. 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
• ~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
Work Alongside Industry Leaders
Diego Protas – Director of Engineering
Diego, an expert in distributed systems and hardware architecture, merging physical computing with enterprise-scale infrastructure. Previously directing teams of 170+ engineers at Mercado Libre and orchestrating large-scale ML-based inference at Meta. At Relay, Diego’s infectious enthusiasm and hands-on leadership are redefining the boundaries of speed and reliability.
Tech Stack Highlights
- Python, Rust and TypeScript - we keep things simple but use the right tool for the job
- Cross-platform Flutter apps with a deep focus on user experience
- Cloud-native on GCP with extensive use of BigQuery and Cloud Run
- Extensive use of ML modelling and LLM inference - no gimmicks here, this is our daily routine
- Emerging tech integrations, including robotics and IoT-powered operations
What you’ll do
- Embed with enterprise clients during onboarding to deliver end-to-end technical success: integration, data quality, metrics alignment, and operational readiness.
- Partner closely with AMs, and external clients to define and implement a delivery performance metrics spec, and build client-specific dashboards that track these metrics in real time for both the client & Relay
- Improve and extend Relay’s client integrations (APIs/webhooks/data feeds), increasing reliability, reducing exceptions, and lowering cost per shipment.
- Build tooling for integration observability (dashboards, alerting, reconciliation, replay) and drive measurable reductions in integration-related incidents.
- Stand up forecast ingestion and validation workflows to improve network planning accuracy and service levels.
- Feed repeatable learnings back into Relay’s platform (templates, SDK patterns, metric contracts, connector blueprints) to scale onboarding and reduce bespoke work.
What success looks like
- Client metric alignment achieved with signed-off definitions and automated reconciliation.
- Material reduction in AM/Ops time spent on reporting disputes and manual data work.
- Improved integration uptime, lower error rates, faster issue resolution (clear SLOs).
- Forecast accuracy and timeliness improves; operational planning volatility decreases.
- New client onboarding time and marginal integration cost goes down quarter over quarter.
Ideal background
- Strong software fundamentals (APIs, distributed systems basics, data pipelines).
- Customer-facing maturity: you can run a room with technical + business stakeholders.
- Bias to shipping: you turn ambiguity into production outcomes quickly.
- Comfort in chaotic reality: inconsistent data, changing requirements, operational pressure.
Fast and Focused Hiring Process
1. Talent Acquisition Interview - 30 min
2. API Integration Interview - 1 hour
3. Technical Interview - 2 hours
4. Operat
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