Staff Machine Learning Engineer

The opportunity

We are building the next generation of AI-driven game experiences — generative world models, neural rendering, and multi-modal understanding that turn images, text, and 3D primitives into interactive worlds. 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.

What you'll be doing

Technical Leadership

• 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.

Architecture \& Research Translation

• 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.