Senior/Staff Software Engineer - Data Platform

Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further.

We're hiring a Senior or Staff Software Engineer to join our Data Engineering team based in London.

As the Data Platform Team, we strive to create the world’s leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.

We’re looking for experienced Engineers to help build our data platform from the ground up. You’ll have the opportunity to define its architecture, make key decisions, and shape how we handle and process petabyte-scale data in the near future.

• Build the Capability Factory - an internal platform designed for everyone from software engineers to non-technical operators, enabling the entire organization to teach HMND robots new skills at scale, from raw data all the way to deployed capabilities.

• Curate, preprocess, and manage large-scale datasets for humanoid robot training - a corpus of robot telemetry growing toward petabyte scale.

• Design and operate highly scalable data pipelines and the compute infrastructure that powers them, ensuring reliability and throughput as data volume and team demands grow.

• Ensure the quality, accuracy, and consistency of training data across multiple concurrent projects and robot platforms.

• Collaborate with machine learning teams to shape the Capability Factory, streamline MLOps, and build the evaluation workflows that close the loop between training runs and real-world robot performance.

• Build data warehouse solutions and BI dashboards that give stakeholders across the organization clear visibility into data collection, model progress, and operational health.

• Establish and uphold best practices for data management - versioning, access control, security, and compliance.

• 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.

• Competitive equity: stock options with meaningful upside as we scale.

• 30+ paid days off, including 23 days of 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.