Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Lead modeling projects to enhance customer engagement. Build and optimize machine learning models, mentor junior scientists, and communicate findings to stakeholders.

The Lead Machine Learning Engineer will design and deploy ML models, optimize AI systems, and collaborate with cross-functional teams to enhance banking solutions.

As a Senior Machine Learning Engineer in the Health team, you'll design and productionize ML systems for personalized health metrics, collaborating with data scientists and engineers to optimize performance and ensure service reliability.

The role involves deploying ML models for ads, collaborating with teams to enhance solutions, and mentoring junior scientists, requiring advanced technical and communication skills.

The Machine Learning Engineer will design, implement, and manage complex ML algorithms, focusing on computer vision and data analysis while collaborating with team members to transition algorithms to production.

Work on prototyping and implementing audio ML algorithms for real-time applications. Present findings and integrate solutions into existing platforms. Collaborate with experts in ML and DSP.

As a Lead AI Engineer, you will design, develop, and scale backend and ML systems, mentor engineers, and collaborate to create predictive ML solutions.

As a Senior Machine Learning Engineer, develop solutions for robotic perception, focusing on rare events in warehouses using cutting-edge AI and ML techniques.

The Senior Machine Learning Engineer will design multi-modal vision systems, optimize and deploy models, mentor junior engineers, and improve data strategies for robotic perception tasks.

The Machine Learning Engineer Intern at Zoox will work on perception systems, developing algorithms for autonomous driving, collaborating with teams, and leveraging advanced machine learning models and datasets.

Design, implement, and deploy ML models and scalable data pipelines for multimodal behavioral and clinical data. Clean, integrate, analyze, and visualize time-series and wearable data; build real-time dashboards/apps; evaluate model performance; supervise analyses; collaborate with IT; and support publications and grant preparation.