> Markdown version of [/jobs/ext/4620938-staff-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4620938-staff-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** Workman Dental Labs - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $218,400 - $327,600 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, Game Engine, Python (Programming Language), Machine Learning, Language Modeling, Software Architecture, NeRF (Neural Radiance Fields), Neural Rendering, Pytorch, Discretization, Low Latency, ONNX (Open Neural Network Exchange) Format, Stable Diffusion, GPT - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25304_fa71f2099fc70354c36ecead4493458e ## About the Role * 6+ years ML engineering with significant depth in computer vision and/or multi-modal modeling * Production experience with transformer-based and diffusion vision models (ViT, CLIP, Stable Diffusion, DETR, SAM) * Strong full model lifecycle knowledge: data curation, training, evaluation, serving at scale * Familiarity with efficient attention, diffusion samplers, multi-modal fusion, and vision-language alignment Unique * Strong Python and PyTorch skills; proven technical leadership track record * Preferred: world-model/video-generation/neural rendering pipelines (NeRF, 3DGS), on-device deployment (CoreML, TFLite, ONNX), real-time graphics or game engine background Common ## Description * Establish technical vision and roadmap for computer vision and multi-modal AI (transformers, diffusion models, vision-language models) * Drive design of systems for image/video understanding, generation, segmentation, detection, and multi-modal reasoning * Make architectural decisions balancing quality, latency, and cost across cloud, server, and on-device targets * Own the full pipeline from research prototypes to production: training, fine-tuning, distillation, serving * Apply compression, quantization, pruning, and knowledge distillation for constrained environments Unique * Lead and mentor ML engineers; define and track KPIs for model quality, accuracy, latency, memory, and cost