Top Machine Learning Engineer Jobs in Boston, MA

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

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.

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.

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

As a Staff Machine Learning Engineer, you will build and manage production-grade ML systems, ensure scalable deployment, and lead the AI Platform team in developing robust tools and services for identity security 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.

Design and implement RAG pipelines, build retrieval systems, develop LLM solutions, and automate ML systems lifecycle for Kensho's AI projects.

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 Machine Learning Engineer, you'll enhance ML productivity, improve model deployment, and implement algorithms for credit decisioning systems.

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

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

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

Lead productionizing machine learning applications and systems, collaborate with cross-functional teams, and design ML models and infrastructure for business solutions.

As a Lead Data Scientist, you will design and deliver machine learning solutions, collaborating with clients and managing projects independently to solve complex data challenges.

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.

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

As a Machine Learning Engineer III, you will develop and manage ML systems, collaborate with ML scientists, and ensure production reliability and scalability while leading projects that significantly impact supply chain and support operations.