The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.
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.
- 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.
- 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.
- 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.
Similar Jobs
.png)
eCommerce • Fintech • Hardware • Payments • Software • Financial Services
Design, build, scale, and operate high-volume payment APIs and distributed systems serving global traffic. Lead complex engineering projects from requirements through production, improve reliability, performance, observability, fault tolerance, and security, and participate in on-call and incident response. Collaborate across product and engineering teams, use AI-assisted development tools responsibly, and mentor engineers through technical reviews and documentation.
Top Skills:
AWSGoKafkaKotlinTypescript
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
Design, build, scale, and operate high-volume payment APIs and distributed systems serving global traffic. Lead complex engineering projects from requirements through production, improve performance, observability, reliability, fault tolerance, and security, and participate in on-call incident response. Collaborate across product and engineering teams, use AI-assisted development tools responsibly, and mentor engineers through technical reviews, documentation, and guidance.
Top Skills:
AWSGoKafkaKotlinTypescript
Artificial Intelligence • Cloud • Sales • Security • Software • Cybersecurity • Data Privacy
Drive pipeline and revenue growth through digital prospecting, lead qualification, relationship building, and SaaS sales. Engage prospective enterprise customers, manage inbound and outbound leads, use tools such as LinkedIn, 6Sense, Outreach, ZoomInfo, and Salesforce, and collaborate with account executives, sales engineers, channel partners, and marketing. The role includes booking discovery calls, creating opportunities, meeting funnel and pipeline KPIs, and closing transactional deals and upsells.
Top Skills:
6SenseAILinkedInMachine LearningOutreachSaaSSalesforceZoominfo
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
