Research Engineer - Data Infrastructure/ML

• Strong programming background in Python.
• Proficiency in distributed data-processing technologies (e.g., Ray, Apache Spark, Flyte, Dask).
• Hands-on experience with cloud infrastructure (AWS, GCP, or Azure), Kubernetes, and distributed training frameworks (Ray, RLLib, PyTorch DDP, or Horovod).
• Knowledge of dataset versioning, experiment tracking, and reproducibility tools (DVC, MLflow, etc.).
• Exposure to simulation, robotics, or autonomy testing pipelines.
• Experience with deep learning frameworks and ML workflows (PyTorch).
• Familiarity with 3D or robotics data formats (point clouds, meshes, radiance fields, LIDAR).
• Contributions to open-source ML/infra projects or publications in top-tier AI/ML/CV venues.
• Experience with frontend / UI work.

• Competitive salary & stock options - Everyone is an owner and shares in our success.
• Pension / retirement plan - Company contributions to support your long-term financial wellbeing.
• Health & wellness - Comprehensive health, dental, and vision insurance (with regional equivalents for employees outside the UK).
• Flexible time off - Generous holiday allowance plus local public holidays.
• Hybrid-first culture - State-of-the-art London workspace with the flexibility to work remotely.
• Workspace support - Latest hardware and home office equipment to set you up for success.
• Learning & development - Budget and time for conferences, courses, and workshops to keep you at the frontier of ML and 3D AI.
• Community & connection - Team offsites, events, and opportunities to connect with global colleagues.

Third Dimension is building SuperSim, a new kind of simulator which can enable fast, cost-effective and photorealistic 3D simulations directly from source data using AI. We are working with customers across multiple industries, including autonomous vehicles, drones, robots in industrial and manufacturing environments, and more.

We're looking for a Data Infrastructure / ML Engineer to build the backbone of our 3D AI systems. You'll design scalable pipelines for 3D, video, image, and other sensor data (e.g., LIDAR) and develop the ML workflows that power SuperSim. Your work will enable our researchers to train, validate, and deploy models faster - ultimately shaping how robots and autonomous systems are tested in the real world.

In this role, you will:

• Build and maintain high-performance data pipelines to ingest, transform, and version multi-modal datasets (3D, video, sensor).
• Design and optimize distributed training and data-processing infrastructure - across cloud and containerized environments (Kubernetes, Ray, Dask, EKS, Buildkite).
• Collaborate with researchers to productionize ML models (PyTorch), from prototype to deployment.
• Develop tools and APIs that make data discoverable, reusable, and reproducible.
• Monitor and improve data quality, lineage, and performance across the ML lifecycle.

• London, UK / Graz, Austria/ Bay Area: This is a priority for us, as we're trying to build a presence around our already existing teams in London, Graz or the Bay area. Hybrid (remote + in office days) preferred.
• Remote: If hybrid is not an option, but you're amazing, we're open to having you work out your home location.