3D Artist - OpenUSD & Synthetic Data · AI/ Computer Vision

About Surveily

Surveily develops AI safety and operations intelligence for industrial workplaces. Its AI and video-analytics product uses existing camera infrastructure to detect hazards in real time and help prevent incidents. The platform integrates with 95% of existing digital camera infrastructure; customers have reported a 1152% increase in unsafe-behaviour visibility, 72% fewer safety incidents, and a 25% reduction in LTIR.

About the role

Object-detection models (YOLO) are trained largely on synthetic data: photorealistic 3D scenes of industrial environments, including warehouses, production halls and yards, populated with people, forklifts, machinery and PPE. An existing Python-native SimulationApp drives Synthetic Data Generation from an established OpenUSD scene and asset library.

You will own and make this pipeline dependable: create new Blender assets, maintain and extend the USD library, and run SDG batches that deliver clean, training-ready datasets to the ML team on time.

The challenge

Your work has downstream consumers: automated pipelines and ML engineers. A broken hierarchy, missing semantic label or scene that only works locally can lead to failed generation runs, wasted GPU hours and models that miss hazards in the field. Visual quality matters, but consistency, technical correctness and predictable delivery matter more. The asset library should be treated like production code: versioned, validated and documented, with datasets handed off on schedule.

What you'll own

Asset Creation (Blender)

• Model, texture and optimise new 3D assets: industrial equipment, vehicles such as forklifts and trucks, people, PPE, props and environmental elements.
• Export assets to USD cleanly and consistently, with correct scale, pivots, hierarchies, naming, materials and semantic labels required for SDG annotation.
• Keep polycount, texture budgets and LODs suitable for large-scale batch rendering.

USD Library Stewardship

• Edit, repair and extend existing OpenUSD assets and scenes, including composition arcs, references, payloads, variants, layering, materials and semantics.
• Maintain a versioned, documented library with a predictable structure that enables reliable asset reuse.
• Validate every asset before it enters the library, identifying broken references, incorrect units and missing semantics before they cause a failed training run.

Synthetic Data Generation

• Own and operate the SDG process end to end: configure and launch generation batches, select scenes, apply domain randomisation for lighting, camera poses, textures and placement, and produce annotation outputs for YOLO training.
• Monitor generation runs, verify output and label quality, and hand off clean, documented datasets to the ML team on schedule.
• Work with ML engineers to translate model underperformance on a class into asset or randomisation changes and regenerate data.

What you'll bring

• 3+ years of professional 3D experience in games, VFX, simulation, archviz or synthetic data, with strong Blender modelling, UV, texturing and materials skills.
• Hands-on OpenUSD experience, including composition with references, variants and layers; ability to debug broken stages; and understanding of how hierarchies and up-axis settings affect pipelines.
• Working Python knowledge to run, configure and lightly modify pipeline scripts.
• Computer-vision awareness, including annotation types, domain randomisation and the sim-to-real gap for detection models.
• High ownership and discipline: plan work, communicate status honestly, flag issues early and deliver reliably.
• Communicative English and Polish for day-to-day collaboration.

Nice to have

• Experience with NVIDIA Omniverse, Isaac Sim or Replicator.
• Experience training or evaluating detection models such as YOLO.
• PBR workflows, Substance Painter/Designer or photogrammetry.
• Git or other version control for asset management.
• Comfort using AI assistants such as Claude, ChatGPT or Copilot for scripting, debugging and learning tools.
• Exposure to industrial, logistics or safety environments.

What we offer

• A role where generated datasets train models designed to prevent injuries on industrial sites.
• Full ownership of the 3D content and SDG pipeline, with improvements used in production models.
• An established, working pipeline rather than a greenfield setup.
• Direct collaboration with the ML team and founders, with short feedback loops and minimal bureaucracy.
• Access to frontier AI models, including Claude, for Python scripting, USD debugging and Omniverse work.
• Workstation-class GPU hardware and required tools.
• Hybrid work from the Wrocław office.
• B2B contract with compensation matched to experience.

Recruitment process

Only selected candidates whose CV and portfolio clearly demonstrate the required skills will be contacted. The process includes a CV and portfolio review, a 5-minute phone call, a 30-minute video interview, and a practical task: model a small asset in Blender, export it to USD according to the required specification, and explain the validation checks performed. Candidates will receive an update at the end of each stage.

Please include a portfolio or asset samples and, if possible, an example of USD or pipeline work with your application.