Role: Senior Machine Learning Engineer (ML Infrastructure & Data Systems)

Location: New York, NY (Onsite)

Our client is an early-stage robotics and AI company building autonomous systems that operate in real-world industrial environments. Their platform focuses on automating complex, mission-critical workflows using advanced machine learning and robotic systems.

The company is already deploying production-grade systems in live environments and is now entering a rapid scaling phase. Their approach emphasizes fast iteration, learning from real-world data, and continuously improving system performance through tight feedback loops between deployment and model training.

They are building toward large-scale deployments across industrial settings, with a long-term vision of making advanced automation broadly accessible through intelligent, adaptive robotic systems.

Position Overview:

This role will own the machine learning infrastructure and data platform that powers large-scale model training and deployment. As data volume rapidly grows, this person will design and scale systems that ingest, process, and serve massive multimodal datasets (including video) for real-time and offline training.

The ideal candidate combines deep experience in large-scale data systems with strong intuition for machine learning infrastructure, particularly in environments where reliability, performance, and iteration speed are critical.

Key Responsibilities:

• Own and scale the data platform to ingest high-volume streaming data (e.g., video) and make it available for real-time training workflows
• Build and manage end-to-end ML infrastructure, including distributed training, experiment tracking, and compute orchestration
• Design high-performance data access layers and storage systems for petabyte-scale multimodal datasets
• Partner closely with research teams to design, run, and iterate on experiments improving model performance
• Ensure reliability, scalability, and high availability of critical ML and data infrastructure systems

Qualifications:

✅ You likely fit if you:

• Have deep experience building and operating large-scale (PB+) data systems
• Are comfortable with real-time processing, streaming pipelines, and event-driven architectures
• Have built and scaled ML training and inference systems in production environments
• Thrive in high-autonomy environments with minimal oversight
• Are motivated by fast-paced environments with high ownership and impact
• Have a strong interest in robotics, autonomy, or physical AI systems
• ➕ Nice to Have:
• Experience with large-scale datasets in robotics or autonomous systems
• Hands-on experience with distributed training at scale (e.g., large GPU workloads)
• Familiarity with video processing, compression, and efficient storage systems
• Experience with reinforcement learning, imitation learning, or multimodal model pipelines
• Exposure to hardware-integrated systems in production environments

❌ This role is not for you if:

• Prefer narrowly scoped roles without ownership of systems end-to-end
• Are uncomfortable working with large-scale, high-throughput data systems
• Prefer highly structured environments with clearly predefined requirements

What they offer:

• Base salary: $175k – $250k
• Equity participation
• Comprehensive benefits package
• Opportunity to build foundational infrastructure for real-world AI systems at scale
• High-impact role within a fast-growing, early-stage company