Senior Staff Engineer Machine Learning

About the Role Senior AI Researcher/ Senior Staff Engineer to act as a technical beacon for Motorola Solutions' AI Engineering organization. Lead the research strategy for Fine-Grained Visual Recognition and Data-Centric AI, bridging academic research with production-grade solutions while pioneering Generative AI and Agentic Workflows to improve data curation and synthesis. Focus on building next-generation capabilities like semantic scene understanding and open-vocabulary search. What You'll Do

• Advanced Model Research: Lead the R&D of high-performance models for Fine-Grained Visual Categorization (FGVC) and Hierarchical Classification, focusing on detecting complex object attributes and nuances in unconstrained environments using supervised and self-supervised learning.
• Generative AI & VLM: Spearhead exploration of Vision-Language Models (VLM) and CLIP-based architectures for Text-to-Image / Image-to-Image retrieval and Open-Set Recognition.
• Agentic Data Workflows: Architect AI Agentic Workflows to automate the data lifecycle, including autonomous data mining, auto-captioning, and synthetic data generation to address long-tail edge cases.
• Production-Grade AI: Drive transitions from research prototypes to production; collaborate with Embedded AI Engineers to distill and quantize heavy Foundation Models for edge deployment.
• Research Leadership: Act as a lead technical contributor, identify and drive strategic research opportunities, and influence the team's direction through deep technical expertise and hands-on experimentation. What We're Looking For
• Basic Qualifications: Bachelor’s Degree or higher in Computer Science, Artificial Intelligence, Computer Vision, Mathematics, or related field; 7+ years of relevant industry experience in Computer Vision and Machine Learning; proven track record deploying AI models into commercial products; demonstrated ability to lead and mentor technical teams.
• Specific Knowledge/Skills: Deep Learning mastery (Transformers ViT, Swin; CNNs EfficientNet, ResNet; Object Detection/Segmentation); Generative AI & Multimodal (CLIP, LLaVA; Diffusion Models, GANs); Data-Centric AI (automated data pipelines, Active Learning, Auto-labeling, Agentic data curation); Frameworks (PyTorch preferred; TensorFlow); Languages (Python, C++); Practical MLOps (CI/CD for ML, Experiment Tracking). Travel/Relocation: None. Note: 0-to-1 product lifecycle experience is highly preferred.