Staff Machine Learning Engineer

Unity Technologies SF is hiring a Staff Machine Learning Engineer to build production-grade AI for game experiences with a focus on computer vision and multi-modal modeling. Responsibilities Set technical vision and roadmap for computer vision and multi-modal AI models, covering transformers, diffusion models, vision-language models, and JEPA-style generative architectures Design and implement models for image and video understanding and generation, including segmentation, detection, and dense prediction Develop multi-modal reasoning over images, text, and 3D inputs Make architecture, training, data pipeline, and evaluation trade-offs balancing quality, capability, latency, and cost across cloud, server, and on-device targets Drive research-to-production delivery: training, fine-tuning, distillation, export, and serving for deployment scenarios from cloud GPUs to efficient on-device inference Partner with research scientists to translate novel CV and multi-modal architectures into deployable, well-engineered implementations Build scalable multi-modal inference systems that ingest diverse inputs (images, video, text, primitives, and metadata) and produce outputs ranging from semantic predictions to pixel-level generation Monitor and adopt field breakthroughs including vision-language pretraining and alignment, efficient diffusion approaches (consistency models, flow matching), efficient attention (FlashAttention, linear-attention variants), and vision tokenization/representation learning Where needed for latency or device constraints, apply compression and optimization such as compression, quantization, pruning, and knowledge distillation, and integrate runtimes like TensorRT, ONNX Runtime, CoreML, and TFLite Lead and mentor ML engineers, establishing engineering best practices, code review standards, and rigorous benchmarking and evaluation methodology Collaborate with research, platform engineering, product managers, and runtime teams to align ML capabilities to product roadmaps and target-platform constraints Define and enforce measurement practices using KPIs for model quality, accuracy, latency, memory, and cost Requirements 6+ years of ML engineering with strong depth in computer vision and/or multi-modal modeling Production experience with transformer-based and diffusion-based vision models (examples: ViT, CLIP/SigLIP-style encoders, Stable Diffusion, DETR/SAM-style architectures) End-to-end model lifecycle experience including data curation, training and fine-tuning, evaluation, and serving at scale Familiarity with efficient attention, diffusion samplers, multi-modal fusion, and vision-language alignment methods Strong Python skills and modern deep-learning tooling such as PyTorch, plus solid software engineering fundamentals Proven technical leadership: setting direction, influencing cross-functional partners, and growing engineers Technologies Python, PyTorch TensorRT, ONNX Runtime, CoreML, TFLite FlashAttention ViT, CLIP, SigLIP Stable Diffusion DETR, SAM 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 Additional information Location: Mountain View, CA (onsite) Salary (USD per year): USD 172,200 - 283,900 Zone A: $218,400 - $283,900 Zone B: $194,100 - $252,300 Zone C: $172,200 - $223,900 Beyond base salary, the role may be eligible for equity awards and participation in company incentive plans (including annual discretionary bonuses or sales commissions) Final offer depends on geographic location, relevant experience, professional background, and skill set You might also have Experience with world-model, video-generation, or neural rendering pipelines (NeRF, 3DGS, or similar) Experience deploying models to constrained or on-device targets, including quantization (INT8/INT4/FP16), pruning, distillation, and runtimes such as CoreML, TFLite, ONNX Familiarity with mobile SoC accelerators (Apple Neural Engine, Qualcomm Hexagon/Adreno, ARM Mali) or compiler stacks such as MLIR, TVM, or XLA Contributions to open-source ML frameworks or peer-reviewed CV/ML research publications Background in real-time graphics or game engine pipelines (Metal, Vulkan, OpenGL ES) #J-18808-Ljbffr