Software Engineer - Backend - UK

Engineers today are building some of the most ambitious machines humanity has ever seen. They should have AI that’s purpose-built for them. Nominal is building the Hardware Intelligence Platform that gives engineers increased visibility into complex physical systems, helps them make better decisions, and ultimately gets their products into the real world faster. It brings together the systems, data, intelligence, and applications hardware engineering requires to meet the AI moment. Hardware engineering teams use Nominal to simulate, test, manufacture, and operate the physical systems that keep the world moving, from spacecraft and autonomous vehicles to next-generation energy systems and robotics. Our customers span aerospace, defense, manufacturing, transportation, energy, and robotics. They include Anduril, Shield AI, Wisk Aero, Pratt Miller Motorsports, Antares, and Shinkei Systems, amongst others, alongside programs within the U.S. Air Force and Navy. Our team draws from SpaceX, Palantir, Anduril, Applied Intuition, Tesla, and other leading companies, united by a common mission: giving hardware engineers the tools to build the future with speed, safety, and confidence. We’re a fast-moving team that owns problems end-to-end, works across disciplines, and thrives at the intersection of hardware and software. Some of the world’s leading technology investors, including Sequoia, Lightspeed, Founders Fund, General Catalyst, Lux Capital, and Insight Partners, are backing Nominal’s ambition to change how machines are built and operated. We’re looking for engineers who want to take on hard problems and help make a new generation of world-changing hardware possible.About the role Nominal’s backend runs on a set of Platform teams that own everything below the product surface: ingesting and storing the world’s hardware data, making it queryable at scale, and keeping the whole system running across cloud, on-prem, and air-gapped environments. As a Backend Software Engineer, you’ll join one of these teams and design and build the distributed infrastructure that powers the telemetry and validation platform, systems that ingest multi-modal data (time series, video, point clouds, asset and run catalogs), serve it back at high scale, and hold up under real production load from customers. This role is ideal for engineers who enjoy working close to the metal of distributed systems, reasoning about throughput, latency, data models, and failure, and who want to see their work directly enable real-world hardware. You’ll own critical backend systems end to end, from ingestion pipelines to storage engines to performance tuning in production. What you’ll do

• Architect and evolve distributed systems that ingest, process, and persist high-frequency telemetry and sensor data.
• Design storage and indexing strategies for large, time-series, and event-driven datasets with strict performance and correctness requirements.
• Tackle hard problems around scalability, backpressure, fault tolerance, and data consistency in real-time systems.
• Optimize hot paths, reduce tail latency, and improve reliability as the system scales with new customers and use cases.
• Collaborate closely with product, infrastructure, and customer-facing engineers to solve domain-specific problems for cutting-edge hardware teams.
• Influence backend architecture and technical direction, mentor teammates, and raise the bar for system design across the org.

• 3+ years building distributed systems where performance, reliability, and operational simplicity matter.
• Strong experience in Go, Rust, or Java, with an emphasis on correctness, clarity, and long-lived systems.
• Deep familiarity with cloud infrastructure (AWS, GCP, or Azure) and production tooling like Kubernetes, Docker, and Terraform.
• Hands-on experience designing or operating storage systems: time-series databases, columnar formats, or analytics engines (e.g., ClickHouse, TimescaleDB, Druid, InfluxDB, Parquet).
• Experience with streaming or data processing technologies such as Kafka, Apache Arrow, Flink, or Spark.
• Comfort reasoning about distributed systems tradeoffs: latency vs. throughput, consistency vs. availability, abstraction vs. control.
• Clear technical communication and a bias toward ownership: you design it, build it, ship it, and operate it.