Staff Machine Learning Engineer, Unity

• 6+ years in ML engineering, with significant depth in computer vision and/or multi-modal modeling
• Proven production experience with transformer-based and diffusion-based vision models (e.g., ViT, CLIP/SigLIP-style encoders, Stable Diffusion, DETR/SAM-style architectures)
• Strong command of the full model lifecycle: 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 and modern deep-learning tooling (PyTorch); solid software
• Engineering fundamentals
• Track record of technical leadership: setting direction, influencing cross-functional partners, and growing engineers

Desirable

• As our Staff Machine Learning Engineer, you will be a core technical leader bringing state-of-the-art computer vision and multi-modal models — transformers, diffusion networks, vision-language models (VLMs), and JEPA-style architectures — from research into robust, production-grade systems
• This is a deeply hands-on, high-impact role. You will help define the modeling and\ deployment strategy, drive architectural decisions across the ML stack, and mentor a team\ of senior and mid-level engineers.
• Your work will directly shape the quality, capability, and\ performance of AI features experienced by billions of players — across cloud, server, and\ on-device targets
• Help set the technical vision and roadmap for computer vision and multi-modal AI models, spanning transformers, diffusion models, vision-language models, and JEPA-style generative architectures
• Drive design and implementation of models for image and video understanding, generation, segmentation, detection, and dense prediction, as well as multi-modal reasoning over images, text, and 3D inputs
• Make sound decisions on model architecture, training strategy, data pipelines, and evaluation — balancing quality, capability, latency, and cost across deployment targets
• Own the path from research prototype to production: training, fine-tuning, distillation, export, and serving, with deployment spanning cloud GPUs through to efficient on-device inference where the product requires it
• Collaborate directly with research scientists to translate novel CV and multi-modal model architectures into deployable, well-engineered implementations
• Design scalable systems for multi-modal inference that process diverse inputs images,
• Video, text, primitives, and metadata — and produce rich outputs from semantic
• Predictions to pixel-level generation
• Track and rapidly adopt breakthroughs across the field: vision-language pretraining and
• Alignment, efficient diffusion (e.g., consistency models, flow matching), efficient attention
• E.g., FlashAttention, linear-attention variants), and tokenization/representation learning
• For vision
• Where latency or device constraints demand it, apply compression, quantization, pruning, and knowledge distillation, and work with appropriate runtimes (e.g., TensorRT, ONNX Runtime, CoreML, TFLite) to meet performance budgets
• Team \& Cross-Functional Leadership
• Lead and mentor a team of ML engineers; define engineering best practices, code review standards, and 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 culture of measurement: define KPIs for model quality, accuracy, latency, memory, and cost, and ensure the team tracks them rigorously