Staff Machine Learning Engineer

Build production-grade AI for game experiences at Unity Technologies SF. This onsite role in Mountain View focuses on taking cutting-edge computer vision and multi-modal research into dependable, scalable systems across cloud, server, and on-device targets, with a team culture centered on measurement, engineering rigor, and cross-functional collaboration.

Responsibilities

• Help set the technical vision and roadmap for computer vision and multi-modal AI models, including 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, plus multi-modal reasoning over images, text, and 3D inputs.
• Make architecture and delivery decisions across quality, capability, latency, and cost for deployment targets, including training strategy, data pipelines, and evaluation.
• Own the path from research prototype to production: training, fine-tuning, distillation, export, and serving, spanning cloud GPUs to efficient on-device inference where needed.
• Collaborate closely with research scientists to translate novel CV and multi-modal architectures into deployable, well-engineered implementations.
• Build scalable multi-modal inference systems that handle diverse inputs (images, video, text, primitives, and metadata) and generate outputs from semantic predictions to pixel-level generation.
• Rapidly adopt field breakthroughs in areas such as vision-language pretraining and alignment, efficient diffusion (consistency models, flow matching), efficient attention (including FlashAttention and linear-attention variants), and tokenization or representation learning for vision.
• When latency or device constraints require it, apply compression approaches such as quantization, pruning, and knowledge distillation, and work with runtimes including TensorRT, ONNX Runtime, CoreML, and TFLite.
• Lead and mentor ML engineers, define engineering best practices and code review standards, and establish rigorous benchmarking and evaluation methodology.
• Partner with research, platform engineers, product managers, and runtime teams to align ML capabilities with product roadmaps and target-platform constraints.
• Champion a measurement culture by defining KPIs for model quality, accuracy, latency, memory, and cost, and ensuring consistent tracking.

Requirements

• 6+ years of ML engineering experience with substantial depth in computer vision and/or multi-modal modeling.
• Production experience with transformer-based and diffusion-based vision models, including examples such as ViT, CLIP/SigLIP-style encoders, Stable Diffusion, and DETR/SAM-style architectures.
• Strong command of the full model lifecycle, 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 techniques.
• Strong Python skills and modern deep-learning tooling (notably PyTorch), plus solid software engineering fundamentals.
• Proven technical leadership, including 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 172,200 - 283,900 per year
• 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 (such as annual discretionary bonuses or sales commissions)
• Final offer amount 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)