Top Machine Learning Engineer Jobs in Boston, MA

As a Machine Learning Engineer II, you will develop and implement ML models for Chewy's advertising systems, optimizing performance and enhancing user experience through data insights and collaboration with product and engineering teams.

Develop and deploy machine learning systems for health insights, collaborating with engineering teams and ensuring system performance and reliability.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

As a Staff Machine Learning Engineer at Zocdoc, you will develop and mentor while working on scalable ML models to enhance healthcare search experiences.

Develop algorithmic features for movement and training, collaborate with teams, mentor junior data scientists, and maintain ML systems for WHOOP.

Responsible for developing and deploying machine learning algorithms, analyzing data for insights, and improving model performance in a collaborative Data Science team.

Lead the MLOps infrastructure team to build cloud-based analytics, establishing AI/ML strategies, and mentoring staff while focusing on performance metrics.

As a Staff Machine Learning Engineer, you'll enhance and innovate Qualtrics' ML systems, collaborating across teams to optimize AI architectures and ensure scalable solutions, while mentoring juniors.

As a Staff Machine Learning Engineer at Coinbase, you'll apply advanced ML techniques, mentor team members, and develop scalable systems to enhance AI functionalities in products.

Lead AI product initiatives on Edge devices, optimize ML models for Edge compute constraints, and develop AI solutions using petabyte-scale data.

The role involves scaling training systems for speech technologies, building internal tools, and innovating data strategies in machine learning.

The Staff Machine Learning Engineer will lead technical strategy, collaborate on product development, ensure operational excellence, and foster team quality and ownership.

Develop and productionize a next-gen Dispatch System, build predictive models, automate processes, and lead a small team in optimizing operations.

The Distinguished Machine Learning Engineer will drive modern technology adoption, solve complex business problems, mentor talent, and lead data engineering initiatives.

Develop and maintain machine learning models for production systems, optimize ML pipelines, and ensure efficient data processing and model performance monitoring.

The Staff Machine Learning Engineer will lead AI system development, fine-tune models, and design infrastructure for scalable applications.

As a Machine Learning Engineer, you'll develop and deploy ML models, lead A/B testing, and communicate insights to both technical and non-technical stakeholders.

Develop and orchestrate intelligent agent frameworks, handle AI lifecycle stages, and collaborate on enhancing user experiences with machine learning solutions.

Lead and develop embedded software solutions for AI-driven safety products, ensuring accuracy of algorithms and guiding technical efforts within the team.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

As a Staff Applied Machine Learning Engineer, you'll design and implement LLM applications, integrating ML features to enhance user experiences and scalable services.

As a Staff Machine Learning Engineer at Coinbase, you'll lead technical strategies, design ML models for risk management, and collaborate on deploying scalable ML systems to enhance security and user experience.

As a Senior Machine Learning Engineer, you'll develop and deploy AI/ML models, collaborate on solutions for drug development, and ensure system scalability and performance.

Design and build hybrid retrieval systems, develop pipelines for operational knowledge, and architect knowledge graphs while collaborating with experts.

The Machine Learning Engineer will develop AI solutions, work with ML systems, collaborate with cross-functional teams, and improve ML model lifecycles.