Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Design and implement AI/ML solutions using Oracle and modern AI tooling; analyze complex problems, mentor junior staff, build client relationships, manage data pipelines, and support deployment, monitoring, and integration of models into enterprise applications while upholding professional standards.

The Staff Machine Learning Engineer will design and build scalable ML systems for health insights, collaborating with cross-functional teams to ensure compliance and quality in production-ready services.

As a Sr. Distinguished Engineer at Capital One, you will lead AI/ML engineering teams, design and build ML applications, mentor engineers, and drive technology innovations in machine learning.

Design, implement, test, deploy, and iterate machine learning and optimization models and systems. Collaborate with data scientists, engineers, and product managers to scale ML solutions, research new techniques, and improve data quality and predictive applications.

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.

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 design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

The ML Scientist will develop machine learning models for climate risk, collaborate across teams, and validate algorithms to improve predictions of weather extremes.

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.

Design, develop, and deploy machine learning models and AI features. Build and optimize ML pipelines for various use cases.