Top Computer Vision Jobs in Boston, MA

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.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Lead pre-sales solutioning for Physical AI, primarily computer vision, video intelligence, spatial intelligence, and vision language models, with supporting robotics expertise. Engage customers, qualify opportunities, design solutions, build business cases, and develop proofs of concept and demonstrations. Enable sales teams, collaborate with technology partners, create technical content, and contribute to developing WWT’s Physical AI practice and repeatable offerings.

Own customer-facing computer vision deployments from proof of concept through production adoption and maintenance. Build data pipelines, edge-device systems, models, and infrastructure using Python, Docker, Kubernetes, Linux, NVIDIA Jetson, and MLOps. Troubleshoot physical-environment challenges such as lighting, calibration, model drift, networking, and hardware constraints. Work onsite with customers, communicate with technical and executive stakeholders, document architectures, and relay field insights to Product and Engineering. The role requires 40%–50% travel.

Lead the development and deployment of production-ready computer vision and deep learning systems for medical imaging. Responsibilities include model development, end-to-end ML pipelines, validation, data strategy, clinical readiness, explainability, regulatory support, cross-functional collaboration, and technical mentorship. The role requires translating research into reliable, clinically validated AI solutions for real-world healthcare environments.

Build and deploy computer vision and machine learning solutions for power grid infrastructure, including defect detection, thermal anomalies, vegetation monitoring, and intrusion surveillance. Responsibilities span research implementation, model experimentation, error analysis, data pipelines, experiment tracking, model serving, testing, monitoring, and client delivery. The role requires productionizing models, adapting research papers, optimizing architectures, and independently owning projects from problem definition through deployment.

Leads advanced computer vision R&D and production engineering for real-time retail edge deployments. Designs and optimizes deep learning, computer vision, and vision-language models; validates algorithms through evidence-based analysis; and deploys high-throughput, low-latency ML pipelines. Responsibilities include TensorRT and CUDA optimization, Docker-based cloud and embedded deployment, image registration and warping, and translating fundamental research into production-grade systems while providing technical leadership.

Design real-world computer vision tasks, build reference solutions and evaluation components in Python, assess AI model outputs, identify failure modes, and develop structured rubrics and guidelines. Collaborate with experts to maintain high-quality training data and evaluation standards for multimodal and generative vision systems. Part-time, remote contractor role (~20 hours/week).

Senior product engineer owning end-to-end product surfaces for a computer vision platform. Ship features from idea to production, prioritize roadmap, design UX details, integrate APIs, improve annotation and model-assisted labeling, and collaborate with engineering and users to drive product impact.

Design, improve, and deploy production-grade computer vision systems for sports video: detection, multi-object tracking, camera calibration, homography, field registration, identity association, and robustness across varied cameras and video quality. Lead experiments, adapt models to new domains, collaborate with data and engineering teams, and drive end-to-end solutions from prototype to production.