Principal Machine Learning Engineer, Unity

• 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), Transformers.js, WebLLM, or TensorFlow.js — including writing/tuning WGSL compute shaders and working within WebGPU's 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 don't 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

Desirable

• 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
• 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, Transformers.js, WebLLM, TensorFlow.js) 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