Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Lead WHOOP's cloud-based core algorithm teams to develop, validate, and productionize models for sleep, recovery, and training. Drive technical vision, cross-functional alignment with Sensor Intelligence and Hardware, improve ML lifecycle practices (experiment tracking, deployment, monitoring), and manage and grow applied ML scientists and engineers to deliver better member experiences.

As a Senior Lead Machine Learning Engineer, you'll design and implement machine learning applications, lead teams, and optimize systems and data pipelines while collaborating in an Agile environment.

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.

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 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 development of predictive models and modeling frameworks for operational, financial, and organizational data. Explore and analyze large datasets, run statistical tests, implement machine learning models, evaluate results, and translate insights into data-driven business strategies. Collaborate with teams, follow software development best practices, and stay current on insurance industry modeling approaches.

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

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

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

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.