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Staff Machine Learning Engineer

Unity Technologies SF Mountain View, CA

Mountain View, CA

Today

$172k - $284k/yr Full time Ai Ml Ai Model Training Artificial Intelligence

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Job Description

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)

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