Machine Learning Data Engineer

As a Machine Learning Data Engineer at Zeromatter, you'll build and own the data foundation that powers machine learning across our simulation platform. You'll design scaled data infrastructure for training pipelines that turn massive multi-modal datasets — text, images, video, point clouds, and 3D geometry — into our next-generation models. This role offers the rare opportunity to shape how the world's most advanced robotics and autonomous systems learn.

Responsibilities

• Build, operate, and scale the data infrastructure and tooling behind our machine learning workflows.

• Own our data infrastructure end to end at scale: cataloguing, batch pipelines, freshness, quality assurance, and analytics.

• Design the metadata, lineage, and search systems that let any engineer find, trust, and understand any dataset.

• Maintain high-quality, balanced, and representative datasets by supporting data science and analytics.

• Work hands-on with multi-modal data — text, images, video, point clouds, and geometry — with a strong emphasis on 3D contexts and real-world data.

• Take new data systems from zero to one: scope them, build them, and run them in production.

• Partner with ML engineers and researchers to understand model data needs and continuously raise the bar on dataset quality.

Qualifications

• A senior engineer with 5+ years of experience building data and/or ML infrastructure.

• A track record of designing and operating scaled data systems, including batch processing, indexing, quality assurance, and analytics.

• Familiarity with multi-modal data (e.g. text, images, video, point clouds, 3d geometry).

• Experience working with data from vendors and custom collection hardware (e.g., planes, drones, or ground vehicles).

• Proven ability to go 0→1: you're comfortable with ambiguity, scoping problems yourself, and shipping systems from scratch in a fast-moving startup.

• Strong software engineering fundamentals and fluency with modern distributed data processing tools.

Nice to Have

• Deep data science skills: dataset curation, statistical analysis, and experience connecting data quality to model performance.

• Model research or training experience — you've trained models yourself and understand what data they need.

• Background in robotics, autonomy, or simulation.