Location: Flexible / Remote (UK or Europe preferred)
Employment Type: Full-time
About the Company
Our client is an ambitious AI and robotics company developing next-generation humanoid systems designed to transform how intelligent automation supports industrial and everyday environments. Their mission is to advance human potential through robotics that are scalable, safe, and capable of performing complex real-world tasks.
This is a rare opportunity to work at the intersection of deep learning, multimodal AI, and robotic embodiment, helping shape the foundations of a truly intelligent automation platform.
The Role
As a Deep Learning Engineer, you’ll design and train large-scale models that power robotic control and perception — from foundational representation learning to behaviour cloning and reinforcement learning. You’ll work across the full data-to-deployment lifecycle, experimenting with cutting-edge multimodal architectures and building robust pipelines for high-performance, real-time systems.
Key Responsibilities
- Develop and train deep learning models for manipulation, navigation, and general policy learning.
- Collaborate with teleoperations and simulation teams to define data collection goals and bridge the sim-to-real gap.
- Train and fine-tune multimodal LLMs, VLMs, and VLAs, integrating diverse sensory modalities (vision, audio, proprioception, LiDAR, etc.).
- Build scalable data pipelines for continuous ingestion, curation, weak supervision, and retraining.
- Partner with MLOps and infrastructure teams to enable distributed training and optimize models for real-time deployment.
- Contribute to shaping the next generation of embodied AI systems for safe, efficient automation.
- 3+ years of experience building and deploying deep learning systems (industry or research).
- Strong proficiency in Python and PyTorch or JAX.
- Hands-on experience with LLMs, VLMs, or generative models for image/video.
- Deep understanding of training infrastructure (streaming datasets, checkpointing, distributed compute).
- Strong communicator with clear experiment documentation and the ability to explain complex technical decisions.
- Experience in robotics, autonomous driving, or other embodied AI domains.
- Background in reinforcement learning (PPO, DPO, SAC, etc.) or RL for LLMs.
- Experience optimizing deep nets for production (latency, telemetry, on-device inference).
- Publications at top-tier ML conferences (ICLR, NeurIPS, ICML) or significant open-source contributions.
- Familiarity with OpenVLA, π models, or similar embodied AI frameworks.
- Competitive compensation including stock options.
- Flexible remote-first setup with opportunities for international collaboration.
- Work with world-class researchers and engineers building truly transformative technology.
- A fast-paced, innovation-driven culture where ideas move quickly from concept to prototype.
