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)