Deep Learning Engineer, Multimodal Vision Platform

Sourced from careercenter.cpp.edu

Matroid, a Stanford-founded computer vision company, is hiring a Deep Learning Engineer to build models and systems at the core of its multimodal vision platform. You will take ideas from research to production, spanning detection, segmentation, anomaly detection, tracking, video understanding, vision-language, and 3D reconstruction. The role is deeply technical with significant ownership and requires onsite work five days a week in downtown Palo Alto.

• Analyze the latest computer vision and deep learning research and adapt promising techniques for the platform
• Design, adapt, and train models for detection, segmentation, anomaly detection, tracking, video understanding, vision-language, and 3D reconstruction
• Turn new models and techniques into platform capabilities customers can train and deploy on their own data
• Optimize architectures and deployment systems for fast, cost-efficient inference from cloud GPUs to edge devices
• Collaborate with product, infrastructure, and field engineering teams to drive capabilities to customer impact

• Master's in Computer Science, Software Engineering, Mathematics, or related field, or Bachelor's with equivalent experience
• 3+ years of industry and/or academic experience in deep learning, computer vision, and software engineering
• Experience building ML systems and capabilities, not just scripts and one-off experiments
• Familiarity with modern foundation models and how they work
• Strong software engineering skills in Python and PyTorch
• Solid oral, written, presentation, collaboration, and interpersonal communication skills

• Significant computer vision and deep learning research experience
• Prior work applying computer vision to real-world use cases
• Experience productionizing models with inference optimization (TensorRT, ONNX) and serving (Triton Inference Server)
• Experience with 3D reconstruction and scene representation (COLMAP, VGGT, 3D Gaussian splatting, world models)
• Experience building or operating multi-tenant ML products
• Open-source contributions to ML tooling, personal projects, competitions, or hackathons
• Previous work at high-growth technology startups