12mo ago

Research Engineer - Data Infrastructure/ML

Londonfull-timemid

OtherResearch EngineerSoftware EngineeringSearch Engineer

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Quick Summary

Overview

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.

Technical Tools

OtherResearch EngineerSoftware EngineeringSearch Engineer

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.

About the Role

~1 min read

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.

Requirements

~1 min read

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.).

Nice to Have

~1 min read

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.

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.