Research Engineer

• Your focus will be defined by the intersection of Robotics and ML and large-scale multimodal model training - expertise in both is optimal and alternatively eagerness to learn.
• Expect challenges across two main pillars with the opportunity to specialise:
• Core Research & Large-Scale Infrastructure
• Own the Training Stack: Design, implement, and maintain the core infrastructure for large-scale VLA model training, including scheduling, distribution, job management, checkpointing, and rigorous logging.
• Enable Rapid Iteration: Build the critical tools and abstractions necessary for launching, monitoring, debugging, and seamlessly reproducing complex, multi-variant experiments.
• Train from Deployment Logs: Utilize our massive repository of offline, classical stack data to pre-train robust robot foundation models.
• Drive the Software Feedback Loop: Translate core research needs into concrete infra capabilities, track experiments, analyze results, and close the loop directly with ML researchers to unblock model progress
• Real-World Evaluation & Operations
• Design Physical Benchmarks: Design new robotic tasks and build lightweight physical setups to systematically evaluate model capabilities far beyond the limits of simulation.
• Execute Structured Evaluations: Ensure robots are properly configured, calibrated, and ready for rollouts. You will coordinate data collection efforts and run structured, on-robot evaluations to measure real-world success rates.
• Close the Physical Feedback Loop: Analyze real-world evaluation results to guide the ML research direction. You will identify operational bottlenecks across software, hardware, and deployment systems to continuously improve our iteration speed.
• Scale the Workflows: Beta test internal and third-party tools for teaching robots new skills, and write clear, structured documentation so the broader team can reproduce your workflows and scale your impact.

• Develop cutting-edge deep learning approaches and apply them to robotic handling of a vast variety of objects.
• Increase our robots' dexterity by collaborating with the hardware team on real-world, physical innovations.
• Productionize novel approaches to anomaly detection (e.g. product disassembling in motion).
• Automate data and model retraining pipelines
• Use data to influence company-level strategic decisions
• Evaluate and monitor ML models in production, detect data drifts and other reasons for degraded performance

Senior Machine Learning Research Engineer

• Explore, reproduce, benchmark and validate ML methodologies
• Work in an R&D / PoC environment
• Collaborate with AI engineers and ML researchers
• Validate model performance and prepare ground truth data

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Research Engineer

Senior Machine Learning Research Engineer

Senior Machine Learning Research Engineer – Benchmarking, Paper Replication