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.
Compensation
Base Salary Range: We determine the base salary range for this role based on your primary work location:
Zone A: $218,400 - $283,900
Zone B: $194,100 - $252,300
Zone C: $172,200 - $223,900
This range reflects the anticipated base salary for this position. Beyond base salary, this role may be eligible for equity awards and participation in our company incentive plans (such as annual discretionary bonuses or sales commissions). The final offer amount will depend on several factors, including geographic location and the candidate’s relevant experience, professional background, and skill set.
Responsibilities
Help set the technical vision and roadmap for computer vision and multi-modal AI models
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
Own the path from research prototype to production: training, fine-tuning, distillation, export, and serving
Collaborate directly with research scientists to translate novel CV and multi-modal model architectures into deployable, well-engineered implementations
Principal Engineer for On-Device AI Inference & Systems, owning the inference and integration stack end-to-end to make state-of-the-art multi-modal models run fast, small, and reliably within a browser-native runtime for game experiences.
Compensation
Zone A: $250,300 - $325,400
Zone B: $222,600 - $289,400
Zone C: $197,400 - $256,600
Responsibilities
Own the end-to-end optimization pipeline: model export, graph transformation, operator fusion, memory-layout planning, and hardware-specific kernel tuning across NPU, mobile GPU, and desktop/laptop GPU.
Make authoritative decisions on quantization (INT4/INT8/FP16), weight sharing, structured/unstructured pruning, and knowledge distillation to hit hard latency, memory, and power budgets.
Drive low-level performance work: write and tune WebGPU compute shaders (WGSL) and, where relevant, native kernels (Metal, Vulkan/SPIR-V compute, D3D12, CUDA); profile with browser and platform tools.
Design and own the integration between the ML runtime and the game engine: real-time scheduling, threading, memory pooling, zero-copy buffer sharing between the inference path and the render path, and frame-budget management alongside the renderer.