2 months ago
London, United KingdomMid Level
Responsibilities
- Post-train policies using behavior cloning and reinforcement learning, owning the process from data through deployment.
- Partner with data collection teams to define quality standards, identify failure modes, and ensure data diversity and coverage.
- Coordinate with external partners to maintain a supply of high-quality pretraining-scale data.
- Run pre-training, mid-training, and post-training on the VLA stack while exploring new modalities and architecture changes.
- Build and maintain pipelines for synthetic data and teleoperation logs, including versioning, weak-supervision labeling, dataset curation, and automated failure-case discovery.
- Collaborate with MLOps and Data Platform teams to scale distributed training and optimize models for real-time edge inference.
Requirements
- 3+ years of experience building deep-learning systems in industry or research, with shipped models or published artifacts.
- Deep hands-on experience with at least one of LLMs, VLMs, or image/video generative models, including architecture, training, and inference.
- Experience with streaming datasets, checkpointing and state management, and distributed training strategies.
- Strong Python and PyTorch or JAX skills, including profiling, numerical debugging, and maintainable research code.
- Familiarity with modern software engineering practices and the ability to document experiments and communicate trade-offs clearly.
- Robotics or autonomous driving experience, reinforcement learning for LLMs or robotics, VLA model experience, deep-net productization, relevant publications, open-source contributions, or familiarity with OpenVLA and Physical Intelligence models are preferred.
Benefits
- Competitive equity through stock options with meaningful upside.
- 30+ paid days off, including 23 days of annual leave, UK bank holidays, and additional company closure days.
- Private healthcare with virtual and in-person care.
- Pension scheme with an 8% total contribution, comprising 5% employee and 3% employer contributions, on full earnings.
- Free daily breakfast, catered lunch, and snacks in the office.
- London-based role with in-office collaboration and access to founding leadership and product ownership.
Categories
ML EngineeringRobotics
