humanoid - Sr./Staff Software Engineer (Data Platform)
Requirements
• 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. • 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.
Responsibilities
• 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.
Benefits
• 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.
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