Overview

We're seeking a Senior Machine Learning Engineer to join our NYC-based team (we are an in-person company), and own our ML infrastructure and data systems. As our robots scale, so does the volume of data we collect—we need someone who can build the systems to ingest, process, and serve petabytes of multimodal data for training our neural network policies. We are an early stage company moving very fast in a rapidly growing space, and welcome people from any background as long as you're excited to join our mission, drive immediate impact, and create a future where automation is accessible to all.

What You'll Do

• Own our data platform and scale it to ingest large amounts of video streams and make them available for training in real time
• Own our ML infrastructure stack end-to-end, including distributed training, experiment tracking, and cluster management
• Build out data management systems and high-performance data access layers on top of PB+ scale multimodal data
• Collaborate with the rest of our research team on designing and running experiments as we rapidly improve our policies’ capabilities
• Maintain high availability and reliability of critical infrastructure

Who You Are

• You have deep experience building and operating large-scale data systems (PB+ scale), ideally in domains like autonomous vehicles or robotics
• You're comfortable with real-time processing, streaming, and event-driven systems
• You have built deep learning training and inference systems at scale
• You thrive in a high-trust, high-autonomy environment. You don't need to be micromanaged on what the top priorities are at any given moment
• You're hungry for impact and personal growth, and like to have fun in the pursuit
• Deeply passionate about robotics and physical AI

Bonus Points

• Experience working on large-scale autonomous vehicles datasets or similar robotics domains
• Hands-on experience with distributed ML training at scale (1000+ GPU hours)
• Familiarity with video codecs, compression, and efficient video storage/retrieval systems
• Experience with reinforcement learning, imitation learning, or VLA model training pipelines