Top Computer Vision Jobs in Boston, MA

Develop and deploy computer vision capabilities, building pipelines for image processing, analysis, and integration with multimodal AI systems. Requires collaboration with Product and Engineering teams.

Develop advanced Vision-Language-Action models for robotaxis, leading data strategies and improving driving behaviors using machine learning infrastructure.

Develop and deploy computer vision capabilities, implement image analytics, support multimodal AI workflows, and optimize performance in a mission-driven environment.

Design and integrate vision encoders and audio-native models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs while ensuring efficient cross-modal alignment.

Bridge customers and engineering to define technical requirements, lead pre-sales demos and PoCs, manage robotics and vision inspection projects end-to-end, deliver technical documentation and ROI analyses, and support onsite installs, training, and post-install support while coordinating integrators and stakeholders.

The Principal Scientist will lead data product vision and execution strategy for imaging in digital media, advancing data-driven capabilities and modernizing imaging ecosystems.

Lead development and deployment of real-time, on-device computer vision models for outdoor home monitoring. Architect, train, and optimize transformer/CNN-hybrid models for embedded cameras, ensuring accuracy, latency, memory, and power budgets. Collaborate with firmware and platform teams, define evaluation strategies, perform failure analysis, improve long-tail robustness, and mentor engineers while setting technical direction and best practices for reproducible edge ML workflows.

Lead the computer vision and tracking systems, mentoring engineers and optimizing models for athlete performance insights, while ensuring reliability of the technology stack.

Develop, optimize, and deploy production-grade computer vision models (depth, segmentation, tracking) for an autonomous mower. Own model lifecycle from data collection and synthetic-data strategies through training, edge optimization (quantization, mixed precision), and embedded NVIDIA GPU deployment. Integrate perception with robotics stack, analyze field failure cases, and continuously improve robustness for challenging outdoor conditions.

Work on vision-language model development and infrastructure: improve VLM understanding, OCR and function-calling workflows, fine-tuning recipes, evaluation and robustness, and optimize training/serving pipelines. Own end-to-end projects from experiments to production-ready code, collaborating on scale, cost, and reliability.