Senior Machine Learning Engineer, On-Device & Mobile AI Optimization

Description

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 a Senior Machine Learning Engineer for On-Device & Mobile AI, you will take state-of-the-art multi-modal models — transformers, diffusion networks, and vision-language models (VLMs) — 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 — from a trained checkpoint leaving research, through export, quantization, and kernel-level tuning, to a shipped feature running inside the engine at interactive frame rates within a fixed memory and power budget. Your work directly shapes the latency, quality, memory footprint, and battery profile of AI features experienced by billions of players. This role is for an engineer who is energized by the gap between a research model and a shipping, on-device product. If you enjoy profilers, frame captures, op-fusion, and shaving milliseconds and megabytes, this is your role. 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 across NPU, mobile GPU, and desktop/laptop GPU. - Apply 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. - Do low-level performance work: write and tune WebGPU compute shaders (WGSL) and, where relevant, native kernels (Metal, Vulkan/SPIR-V compute, 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 - Work with WebGPU-targeted inference runtimes (ONNX Runtime Web, Transformers.js, WebLLM, TensorFlow.js) alongside native options (CoreML, ONNX Runtime, TFLite, ExecuTorch), and extend or build glue code where off-the-shelf options fall short of our diffusion and VLM workloads. - Build parts of the integration between the ML runtime and the game engine: real-time scheduling, memory pooling, zero-copy buffer sharing between the inference and render paths, and frame-budget management alongside the renderer. - Build supporting engineering for your components: 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 with research scientists to turn novel CV and multi-modal architectures into implementations that are deployable, debuggable, and fast on device. - Provide a feedback loop into research: surface hardware const