Principal Engineer, On-Device AI Inference & Systems

The opportunity
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. As our Principal Engineer for On-Device AI Inference & Systems you will be the foremost engineering authority on taking state-of-the-art multi-modal models (transformers and diffusion networks) and making them run fast small and reliably within that runtime fully integrated into a production game engine.

This is a deeply hands-on high-impact engineering role. You will own the inference and integration stack end-to-end from the moment a trained checkpoint leaves research through export optimization and kernel-level tuning to a shipped feature running inside the engine at interactive frame rates within a fixed memory and power budget. You will set the engineering standards drive the architecture of the runtime and integration layers and mentor a team of senior and mid-level engineers. Your work directly determines the latency quality memory footprint and battery profile of AI features experienced by players worldwide.

This role is for an engineer who is energized by the gap between a research model and a shipping AI-based product. If you love profilers frame captures op-fusion and shaving milliseconds and megabytes this is your role.

What youll be doing

• Inference & On-Device Optimization
• Own the end-to-end optimization pipeline: model export graph transformation operator fusion memory-layout planning and hardware-specific kernel tuning across NPU mobile GPU and desktop/laptop GPU.
• Make authoritative decisions on quantization (INT4/INT8/FP16) weight sharing structured/unstructured pruning and knowledge distillation to hit hard latency memory and power budgets and validate them against quality bars.
• Drive low-level performance work: write and tune WebGPU compute shaders (WGSL) and where relevant native kernels (Metal Vulkan/SPIR-V compute D3D12 CUDA); profile with browser and platform tools (Chrome/Dawn GPU traces PIX Instruments/Metal System Trace Snapdragon Profiler Nsight RenderDoc) and eliminate bottlenecks at the op and memory-bandwidth level.
• Apply efficiency techniques dynamic resolution token reduction cross-frame caching/reuse reduced-step diffusion samplers as engineering levers to meet budgets on target SKUs.
• Runtime & Systems Integration
• Evaluate select and drive adoption of WebGPU-targeted inference runtimes (ONNX Runtime Web WebLLM ) alongside native options (CoreML ONNX Runtime TFLite ExecuTorch) and extend or build runtime/glue code where off-the-shelf options fall short of our diffusion workloads.
• Design and own the integration between the ML runtime and the game engine: real-time scheduling threading memory pooling zero-copy buffer sharing between the inference path and the render path and frame-budget management alongside the renderer.
• Architect inference systems that handle diverse inputs images text primitives metadata and produce pixel-level outputs with real-time performance robust to the messy realities of production (cold starts thermal throttling device fragmentation backgrounding).
• Build the supporting engineering: model packaging and asset pipelines on-device fallbacks and SKU-aware capability tiers crash/quality telemetry and automated on-device benchmarking in CI.
• Research Productionization
• Partner closely with research scientists to turn novel architectures into implementations that are deployable debuggable and fast on device.
• Provide the feedback loop back into research: surface hardware constraints op-support gaps and cost models early so model design and deployment converge.
• Track breakthroughs in efficient inference (efficient attention distillation reduced-step diffusion) and assess them pragmatically: what actually moves latency/memory/power on our target devices and what is worth the engineering cost.
• Engineering Leadership
• Lead and mentor a team of engineers; set engineering best practices code-review standards performance-regression gates and on-device benchmarking methodology.
• Champion a culture of measurement: define and enforce KPIs for latency quality memory and power and ensure they are tracked rigorously across the device matrix.
• Partner with platform engineers product managers and runtime teams to align ML capabilities with device-SKU constraints and product roadmaps.

What were 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 experience deploying models through WebGPU e.g. ONNX Runtime Web (WebGPU EP) WebLLM or including writing/tuning WGSL compute shaders and working within WebGPUs adapter device-limits and binding model. Equivalent deep experience with a native GPU/compute API plus a clear path to WebGPU will also be considered.
• 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.
• Low-level performance engineering: strong command of at least one GPU/compute API WebGPU/WGSL Metal Vulkan D3D12 or CUDA and the profiling tools to go with it. You can read a frame capture and a kernel trace and know where the time and memory go.
• Working knowledge of model-optimization techniques quantization (INT4/INT8/FP16) weight sharing pruning and distillation and the practical judgment to apply them to hit latency and memory budgets. You dont need to be a research expert in these methods; you need to use them effectively as engineering tools.
• 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.
• Track record of technical leadership: setting engineering direction influencing cross-functional partners and growing engineers.

You might also have

• Experience shipping world-model neural-rendering or real-time generative pipelines (NeRF 3DGS real-time diffusion or similar) on device.
• Deep game-engine or real-time-graphics background (Unity Unreal or a custom engine; Metal/Vulkan/D3D/OpenGL ES render pipelines) especially integrating compute workloads alongside a renderer.
• Contributions to open-source ML inference frameworks runtimes or GPU/compute libraries especially in the WebGPU ecosystem (Dawn wgpu ORT Web WebLLM).
• Familiarity with the WebGPU specification and its evolving compute features (subgroups FP16/shader-f16 timestamp queries) and the trade-offs of running heavy diffusion workloads in the browser/web runtime.
• Familiarity with compiler stacks (MLIR TVM IREE XLA) for custom kernel generation and graph optimization.
• Experience with on-device benchmarking infrastructure performance-regression CI and large device-farm matrices.

Additional information

• International relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

Benefits
At Unity we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility specific offerings and coverage vary based on the country and employment status.

While specific benefits vary here are some of the ways we strive to take care of our eligible team members globally: 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

Life at Unity
Unity NYSE: U is the worlds 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. For more information please visit .

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This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

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