# Principal Machine Learning Engineer at Unity

- Company: Unity
- What the company does: Develop, deploy, and grow with Unity, the world’s leading 3D game engine. Build for all major platforms from mobile, to PC and console as well as XR, acquire players, monetize your game, and power industrial applications. Backed by Sequoia.
- Company website: https://www.unity3d.com/
- Type: Startups (AI role)
- Level: Principal and up
- Location: Mountain View, CA, USA
- Work setup: On-site
- Pay: $250K to $325K base salary per year (USD)
- Posted: 2026-09-16
- Apply by: 2026-10-31
- Apply: https://unitytech.wd1.myworkdayjobs.com/en-US/Unity/job/Mountain-View-CA-USA/Principal-Machine-Learning-Engineer_JOBREQ-2615941
- Page: https://www.1752.vc/careers/jobs/unity-principal-machine-learning-engineer/

## About the role

We are building the next generation of AI-driven game experiences, running generative models on-device, right where the players are — on phones, tablets, laptops, and desktops. Our games run inside a modern, browser-native runtime (built on technologies such as WebGPU and WebNN), so the models that power these experiences must be deployed and accelerated entirely within that runtime.

## What they're looking for

- 8+ years in software/ML engineering, with at least 4 years focused on on-device / edge inference or real-time, performance-critical systems
- Proven production deployment of transformer- and/or diffusion-based models (e.g., ViT, Stable Diffusion) on mobile, desktop, or embedded hardware — shipped, not just prototyped
- Hands-on expertise with at least one major inference runtime (ONNX Runtime / ORT Web, CoreML, TFLite, ExecuTorch) and deep understanding of operator fusion, memory layout, and runtime scheduling
- Strong understanding of target hardware: mobile SoCs (Apple Neural Engine, Qualcomm Hexagon/Adreno, ARM Mali) and desktop/laptop GPUs (Apple Silicon, NVIDIA, AMD, Intel) — and how to target each for peak throughput
- Proficiency in the core languages of a browser-native runtime — TypeScript/JavaScript and WGSL — plus solid Python for export pipelines and training-side tooling
- Working fluency with the models you deploy — enough to read an architecture, modify it for deployment, and reason about accuracy trade-offs