What You’ll Do

• 5+ years of software engineering experience, with a track record of owning and delivering complex systems end-to-end, not just contributing to them.
• Strong backend engineering - designing and operating production-grade APIs and services: clean data modeling, reliable error handling, performance under load.
• Data engineering at PB+ scale - building and maintaining pipelines that move, transform, and validate large volumes of data reliably; understanding of batch and streaming processing patterns, data quality, and schema evolution.
• Workflow orchestration at scale - designing and operating multi-step automated pipelines with retries, observability, and graceful failure handling.
• Distributed systems fundamentals - you understand how things break at scale: eventual consistency, idempotency, backpressure, job scheduling, and failure modes in distributed compute and storage.
• Cloud infrastructure fluency - you have shipped and operated real systems on a major cloud provider; you think about cost, reliability, and security as first-class concerns, not afterthoughts.
• Container orchestration - deploying and operating workloads on Kubernetes at a level where you can debug scheduling issues, design resource allocation, and reason about cluster health without guidance.
• Full-stack range - comfortable building both the backend and the frontend of an internal product; you can own a feature from database schema to UI without handing off.
• Production ownership mindset - you've been on-call, triaged incidents under pressure, and improved systems after postmortems. You take reliability personally.

Nice to have

• ML infrastructure or MLOps experience - understanding of how training jobs run, how model artifacts are managed, and what makes an evaluation pipeline trustworthy; you've worked alongside or directly supported ML researchers.
• Distributed compute frameworks - experience with large-scale parallel data processing, whether for data transformation, model training, or evaluation.
• Domain knowledge in robotics or embodied AI - familiarity with robot data formats, sensor telemetry, or the sim-to-real evaluation loop is a significant head start.
• BI and data warehouse experience - building data models and dashboards that translate raw operational data into decisions for non-technical stakeholders.
• Dual-cloud or multi-cloud storage - experience reasoning about cost, latency, and consistency tradeoffs across storage providers.
• Frontend product sense - beyond just shipping features, you have opinions about what makes an internal tool actually usable by non-engineers.

Benefits

• Competitive equity: stock options with meaningful upside as we scale.
• 30+ days time off, including 23 days annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).
• Private healthcare, including virtual and in-person care.
• Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings.
• Free daily breakfast, catered lunch, and snacks in-office.
• Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.
• Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.