Hiring: Principal Machine Learning Engineer Company: Unity Location: Mountain View, CA Job Posted Time: 2026-09-15 11:58:17 Target Skills & Keywords : Elasticsearch, Embedded Systems, JavaScript, ONNX, Python, Render, Systems Integration, TensorFlow, Transformers, TypeScript About the job Experience: •8+ years in software/ML engineering, with at least 4 years focused on on-device / edge inference or real-time, performance-critical systems. •At least 4 years focused on on-device / edge inference or real-time, performance-critical systems. Required Skills: •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. •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. •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. •Partner closely with research scientists to turn novel architectures into implementations that are deployable, debuggable, and fast on device. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!