Top Machine Learning Engineer Jobs in Boston, MA

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

Lead a team of developers to deliver AI-driven data solutions, utilizing AWS, Python, SQL, and big data technologies while collaborating across Agile teams.

The role involves designing, developing, testing, and deploying machine learning models, collaborating with cross-functional teams, and monitoring model performance.

As an Applied AI/ML Engineer, you'll develop and deploy machine learning models, build data pipelines, and collaborate with scientists to improve materials processing and characterization.

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

Lead the technical vision for ML systems focused on personalization, architecting solutions, solving complex challenges, and mentoring senior engineers.

Lead the design and deployment of scalable ML infrastructure, focusing on MLOps and LLM strategies, while mentoring team members and ensuring operational maturity.

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

As a Machine Learning Engineer, you will build LLM-driven automation pipelines, develop multi-turn agents for insurance, and fine-tune models using cutting-edge AI technologies.

Design and implement optimizations for llama.cpp, focus on performance analysis, collaborate with open-source community, and enhance machine learning deployments.

Lead the productionization of machine learning applications, focusing on architecture design, model implementation, and optimization. Collaborate with cross-functional teams, automate deployment, and leverage cloud technologies to enhance ML systems.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

As a Machine Learning Engineer, you will design, build, and test deep learning features, optimizing computer vision applications while collaborating with the ML team and improving processes.

As a Machine Learning Engineer at Shipwell, you'll build and scale AI-powered logistics solutions, maintaining data pipelines and ML infrastructure across the organization.

The Lead Machine Learning Engineer will design, develop, and deploy AI software components, collaborate with cross-functional teams, and enhance machine learning models and infrastructure.

The Robotics Machine Learning Engineer will train and deploy visual and language models, manage data flow to optimize machine learning potential, and work collaboratively on improving robotic technologies.

The Principal Machine Learning Engineer will work on vLLM systems, optimize machine learning algorithms, mentor engineers, and enhance AI deployment efficiency.

Design, develop, and validate AI/ML products, collaborate with teams, and support leadership in commercial feasibility, primarily in pharmaceutical applications.

As a Lead Data Scientist, you will design end-to-end analytical solutions, develop ML infrastructure, and collaborate with clients to deliver insights and manage solutions.

As a Senior Generative AI Engineer, you will lead the development of models for synthetic data generation and enhance the safety and capability of autonomous driving solutions at Zoox.

As a Senior Machine Learning Engineer, you will develop machine learning solutions for sensor technology, optimizing object detection algorithms and enhancing image techniques to improve military capabilities.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

The MLOps Engineer will develop scalable ML pipelines, operationalize models, ensure deployment pipelines, and maintain ML infrastructure on AWS, collaborating with teams to ensure model performance and compliance.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.