Deep Learning Engineer
Deep Learning Engineer
Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications.
Our Mission
At Humanoid 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.
About the Role
In this role, you will work on all aspects of training capable policies, be it pre-training of a base model on a diverse multi-embodiment corpus of trajectories, fine-tuning a policy to perform a specific task well, curating data collection processes or exploring productive ways to generate and use synthetic data. This is primarily a deep learning-focused role, so we are looking for experience solving real problems using modern neural networks, while experience in robotics isn’t strictly required. However if you don’t have such experience, be prepared that you’d need to familiarize yourself with a new domain quickly.
What You’ll Do
- Post-train policies via behaviour cloning and RL; own the full loop from data to deployment.
- Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage.
- Work closely with external partners to ensure steady supply of high-quality pretraining-scale data.
- Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes.
- Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining.
- Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference.
We’re Looking For
- 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
- Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.
- Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies.
- Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
- Familiarity with modern software engineering practices.
- You document experiments clearly and communicate trade‑offs crisply.
Nice to have
- Robotics or autonomous driving experience.
- Experience applying RL to LLMs or robotics.
- Experience with VLA (vision-language-action) models.
- Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization).
- Publications at top-tier deep learning conferences or equivalent open‑source contributions.
- Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open source VLA frameworks.
What We Offer
- Competitive salary plus participation in our Stock Option Plan
- Paid vacation with adjustments based on your location to comply with local labor laws
- Travel opportunities to our Vancouver and Boston offices
- Office perks: free breakfasts, lunches, snacks, and regular team events
- Freedom to influence the product and own key initiatives
- Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics
- Startup culture prioritising speed, transparency, and minimal bureaucracy
How to Apply
Does this role sound like the perfect fit for you?
Fill in the form and include links or files that showcase the best of what you’ve built and achieved.
Apply now
*indicates a required field