Senior Machine Learning Engineer; Robotics & Physical AI

Join an early-stage robotics company building industrial AI systems that are already operating in production environments across U.S. warehouses. The team is developing intelligent robots that automate mission-critical warehouse operations, with a strong focus on real-world deployment, rapid iteration, and continuous learning from production data.

This is a highly ambitious, fast-paced environment where engineering excellence and execution matter. The team values builders who enjoy solving difficult technical challenges and want to help shape the future of physical AI and intelligent automation.

Location: New York City (On-site)

What You ll Do

• Own and scale the company's machine learning infrastructure and data platform.
• Design systems capable of ingesting, processing, and serving petabyte-scale multimodal datasets for model training.
• Build and maintain distributed training infrastructure, experiment tracking systems, and compute cluster management.
• Develop high-performance data pipelines supporting video, sensor, and other large-scale robotics datasets.
• Partner closely with ML researchers and robotics engineers to accelerate experimentation and model development.
• Ensure reliability, scalability, and high availability of critical ML infrastructure.

What We're Looking For

• Strong experience designing and operating large-scale data platforms (PB-scale preferred).
• Background building distributed machine learning training or inference systems.
• Experience with streaming, real-time data processing, and event-driven architectures.
• Comfortable owning infrastructure in a fast-moving startup environment with significant autonomy.
• Passion for solving complex engineering problems and building technology that operates in the physical world.
• Excited about robotics, automation, and applied AI.

Preferred Qualifications

• Experience supporting autonomous vehicle, robotics, or other large-scale multimodal datasets.
• Hands-on experience with distributed ML training workloads (1,000+ GPU hours).
• Knowledge of video compression, codecs, and efficient video storage/retrieval systems.
• Familiarity with reinforcement learning, imitation learning, or vision-language-action (VLA) training pipelines.
• Experience integrating software with production hardware systems.

Compensation

• Equity

This is an excellent opportunity for someone who wants to build the infrastructure powering next-generation robotics and physical AI while working on technology that is already making an impact in real-world production environments.