Unity Technologies logo
Unity Technologies

Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law.

Staff Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteLeadTeam 5,001-10,000

Location

United States

Posted

12 days ago

Salary

$167.2K - $250.8K / year

Seniority

Lead

No structured requirement data.

Job Description

Staff Machine Learning Engineer

Unity Technologies

Role Description 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. As a Senior Machine Learning Engineer for On-Device & Mobile AI, you will take state-of-the-art multi-modal models and make them run fast, small, and reliably on mobile and constrained hardware. This is a deeply hands-on role. You will own the optimization and deployment of significant parts of the inference stack, shaping the latency, quality, memory footprint, and battery profile of AI features experienced by billions of players. What you'll be doing - Inference & On-Device Optimization: - Own the optimization pipeline for the models you ship: model export, graph transformation, operator fusion, memory-layout planning, and hardware-specific tuning. - Apply quantization (INT4/INT8/FP16), weight sharing, structured/unstructured pruning, and knowledge distillation. - Do low-level performance work: write and tune WebGPU compute shaders and native kernels; profile with browser and platform tools. - Apply efficiency techniques as engineering levers to meet budgets on target SKUs. - Runtime & Systems Integration: - Work with WebGPU-targeted inference runtimes and extend or build glue code where necessary. - Build parts of the integration between the ML runtime and the game engine. - Build supporting engineering for your components: model packaging, on-device fallbacks, crash/quality telemetry, and automated on-device benchmarking. - Research Productionization: - Partner with research scientists to turn novel CV and multi-modal architectures into deployable implementations. - Provide a feedback loop into research: surface hardware constraints and op-support gaps early. - Track breakthroughs in efficient inference and assess them pragmatically. - Collaboration & Engineering Quality: - Contribute to engineering best practices, code-review standards, and performance-regression gates. - Support a culture of measurement: track KPIs for latency, quality, memory, and power. - Partner with platform engineers, product managers, and runtime teams. - Share knowledge and mentor junior and mid-level engineers. Qualifications - 5+ years in software/ML engineering, with meaningful time focused on on-device / edge inference or real-time, performance-critical systems. - Production deployment of transformer- and/or diffusion-based models on mobile, desktop, or embedded hardware. - Hands-on experience with at least one major inference runtime and a working understanding of operator fusion, memory layout, and runtime scheduling. - Low-level performance engineering: solid command of at least one GPU/compute API and the profiling tools to go with it. - Working knowledge of model-optimization techniques and the judgment to apply them effectively. - Understanding of target hardware: mobile SoCs and/or desktop/laptop GPUs. - Strong Python for export pipelines and training-side tooling; familiarity with core languages of a browser-native runtime is a plus. - Working fluency with the models you deploy. - A collaborative working style: clear communication, reliable delivery, and a willingness to support and learn from teammates. Requirements - Experience shipping world-model, neural-rendering, or real-time generative pipelines on device. - Hands-on experience deploying models through WebGPU. - Game-engine or real-time-graphics background. - Contributions to open-source ML inference frameworks or GPU/compute libraries. - Familiarity with compiler stacks for custom kernel generation and graph optimization. - Experience with on-device benchmarking infrastructure and performance-regression CI. - Proficiency in C++/Objective-C/Swift for runtime integration. Benefits - Comprehensive health, life, and disability insurance. - Commute subsidy. - Employee stock ownership. - Competitive retirement/pension plans. - Generous vacation and personal days. - Support for new parents through leave and family-care programs. - Office food snacks. - Mental Health and Wellbeing programs and support. - Employee Resource Groups. - Global Employee Assistance Program. - Training and development programs. - Volunteering and donation matching program.

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