Overview

In this role, you will work on all aspects of training capable manipulation policies, including pre-training a base policy on a diverse multi-embodiment corpus of manipulation trajectories, fine-tuning models to perform specific tasks, curating data collection processes, and exploring productive ways to use synthetic data. This is primarily a deep learning-focused role; experience solving real problems using modern neural networks is required. Robotics experience is not strictly required, but if you lack domain experience you should be prepared to familiarize yourself quickly.

What You'll Do

• Train policies via representation learning, behaviour cloning and RL; own the full loop from data to deployment.
• Partner with teleoperations to drive data collection: specify what “good” looks like, ensure diversity/coverage, and close the gap between sim and real.
• Run pre-/mid-/post-training on multimodal LLM/VLM/VLA stacks; plug in new modalities (vision, audio, proprioception, LiDAR/point clouds, etc.) without breaking existing ones.
• Build and maintain continuous pipelines: ingest simulation + tele-op logs, version them, apply weak-supervision labeling, 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 deployments.

What We’re Looking For

• 3+ years building deep-learning systems (industry or research) with shipped models or published artifacts to show for.
• Hands-on 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.
• Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
• You document experiments clearly and communicate trade-offs.
• Nice to have: Robotics or autonomous driving; RL for LLMs or robotics (PPO, DPO, SAC); proven productization of deep nets (latency/throughput constraints, telemetry, on-device optimization); publications at ICLR/ICML/NeurIPS or equivalent open-source; familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks (used for more than a year).

What We Offer

• Competitive salary plus participation in our Stock Option Plan
• Paid vacation with adjustments based on 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 prioritizing speed, transparency, and minimal bureaucracy

How to Apply

For more information on the role, or an informal discussion regarding opportunities we have available, please contact Alicja Szymanska on (phone number removed) or email.

About Proactive

Proactive Global is an industry-leading, specialist engineering recruitment agency focused on the automation, manufacturing and advanced technology sectors. We offer specialist recruitment services to a niche customer base, vetting that our clients offer the best opportunities for your career. Proactive encourages and promotes equality and diversity within the workforce. We act with honesty, integrity and impartiality, ensuring your application is considered on its own merits.

When registering with Proactive you will have the opportunity to apply for some of the most interesting, specialist opportunities in the marketplace, with the biggest companies in the sector. Follow us on LinkedIn and Facebook for industry news and download our app for live notifications about newly listed vacancies. Proactive Global is committed to equality in the workplace and is an equal opportunity employer. Proactive Global is acting as an Employment Business in relation to this vacancy.