Top Hybrid Machine Learning Engineer Jobs in Boston, MA

Lead design, build, deploy, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production LLM performance (cost, latency, throughput) and contribute to long-term technical vision.

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.

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.

Embed with Operations and DevOps to identify high-leverage problems and build production-grade LLM-powered automations, agent workflows, and internal tools. Own end-to-end delivery, drive adoption, measure impact, document systems, and extend existing deployed agents to raise company-wide AI fluency.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

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.

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.