Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Design, code, test, and maintain applications using Kotlin, Rust, and SwiftUI. Apply ATDD and CI/CD, implement API management and microservices, perform code reviews and debugging, collaborate with clients on requirements, and support software project planning and business process improvement.

Lead QA activities for AI-enabled applications: design and execute test strategies, identify and resolve defects, perform functional and compatibility testing, validate model outputs and data, automate tests (Selenium), analyze results to inform recommendations, support post-go-live issues, and collaborate with clients and teams while mentoring junior staff.

Lead development and implementation of AI/ML solutions, transform data into insights, design and optimize models, manage projects and budgets, supervise and mentor teams, maintain quality and client relationships, and drive innovation across analytics engineering initiatives.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Lead AI Engineer responsible for developing AI-powered products, optimizing large language model inference, and collaborating across teams to enhance customer interactions and business value.

Design, build, test, and deploy machine learning models and systems; research ML methods; implement scalable ML applications; collaborate with engineers, product managers, and data scientists to deliver production-ready analytics solutions.

Individual contributor on an applied science team building production ML and software systems for media accessibility. Responsibilities include prototyping, evaluation, productionization, experiment design and analysis, code and design reviews, and reporting results for audio/video/NLP/generative AI projects.

Lead end-to-end development and production deployment of ML solutions for claims, using structured and unstructured data. Mentor junior data scientists, follow MLOps best practices, design experiments, engineer features, select algorithms, and communicate results to stakeholders. Contribute to cross-functional initiatives and apply modern ML (including LLMs and computer vision) to improve claims efficiency and outcomes.

Develop and scale machine learning systems to enable AI coaching, collaborate across teams for member insights, and contribute to architectural decisions.

Lead end-to-end machine learning projects to improve customer engagement and retention. Build, test, and deploy models for user forecasting, personalization, and Casino engagement. Partner cross-functionally to scale solutions, mentor junior data scientists, and communicate model impact to influence product and marketing strategy.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Lead training, evaluation, and deployment of ML models for scene understanding and behavior prediction in autonomous vehicles.