Top Hybrid Machine Learning Engineer Jobs in Boston, MA

The Senior Lead AI Engineer will develop, optimize, and support AI products, collaborating with teams to implement machine learning solutions transforming customer interactions at Capital One.

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.

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

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.

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.

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 and build large-scale mining solutions to find challenging driving scenarios, improve learned model performance, and support continuous learning. Collaborate with ML infrastructure to enable iterative mining, error analysis, training and deployment. Research uncertainty and informativeness metrics, run deep learning experiments, provide statistical insight on model uncertainty and robustness, write technical reports, and mentor junior researchers.

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.

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