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

- 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 (e.g., ViT, Stable Diffusion, CLIP/SigLIP-style encoders) on mobile, desktop, or embedded hardware — shipped, not just prototyped. - Hands-on experience with at least one major inference runtime (ONNX Runtime / ORT Web, CoreML, TFLite, ExecuTorch) 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 — 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 reason about where the time and memory go. - Working knowledge of model-optimization techniques — quantization (INT4/INT8/FP16), weight sharing, pruning, and distillation — and the judgment to apply them to hit latency and memory budgets. You use them effectively as engineering tools. - Understanding of target hardware: mobile SoCs (Apple Neural Engine, Qualcomm Hexagon/Adreno, ARM Mali) and/or desktop/laptop GPUs (Apple Silicon, NVIDIA, AMD, Intel). - Strong Python for export pipelines and training-side tooling; familiarity with the core languages of a browser-native runtime (TypeScript/JavaScript, WGSL) is a plus. - Working fluency with the models you deploy — enough to read an architecture, modify it for deployment, and reason about accuracy trade-offs. - A collaborative working style: clear communication, reliable delivery, and a willingness to support and learn from teammates.