Top Hybrid Machine Learning Engineer Jobs in Boston, MA

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.

The ERP AI Engineer - Manager at PwC leads teams in AI/ML engineering, manages client relationships, and designs AI solutions, all while mentoring team members and addressing complex business challenges.

Lead WHOOP's strategy for transforming sensor data into real-time intelligence, managing teams, delivering features, and driving technical direction in machine learning and embedded systems.

Develop predictive analytics and ML solutions for supply chain and commercial teams. Collaborate with product managers, IT, and data scientists to structure data, scale and deploy models, research future-state analytics, and present initiatives to stakeholders.

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 Senior Machine Learning Engineer will design and deploy scalable machine learning models, optimize performance, and lead best practices while collaborating with cross-functional teams.

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.

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.