Staff Machine Learning Engineer - Computer Vision & Multi-Modal AI

Description

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. - 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. What we're looking for - 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 v