Staff Machine Learning Engineer

Job not on LinkedIn

🕒 July 16

🏄 California – Remote

🏄 California – Remote

💵 $218.4k - $283.9k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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UNITYTECH CONSULTING

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

💼 Consulting

Consulting

UNITYTECH CONSULTING is a technology-focused consulting firm (inferred from the name) that appears to provide professional consulting services. There is no company-specific information in the supplied data (the provided text contains unrelated website demo entries for "Outdoor Adventure" themes), so this description is conservative and based primarily on the company name. No details about offerings, customers, or size were provided.

📋 Description

• 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. • 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.

🎯 Requirements

• 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. • 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. • 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.

🏖️ Benefits

• 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