Top AI & Machine Learning Jobs in Boston, MA

Designs, develops, deploys, and supports scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes model performance, GPU/TPU utilization, latency, throughput, and cost; owns complex AI architecture; defines reliability objectives; leads technical reviews; and mentors engineering teams.

Conduct applied research in AI foundations, large language models, and agentic AI. Design, train, evaluate, validate, and deploy foundation models at scale using modern deep learning frameworks and cloud infrastructure. Develop novel methods, deliver platform or product code, collaborate with data scientists, engineers, and product managers, and communicate findings through technical reports or conference publications.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance governance for scalable AI platforms, establish engineering standards, contribute to technical roadmaps, and mentor senior AI engineers. Collaborate with scientists, engineers, technical program managers, and product managers to deliver responsible AI products.

Conduct applied research in AI foundations, large language models, and agentic AI. Build and evaluate foundation models across training, validation, optimization, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products, translate research into business outcomes, and publish technical reports or conference papers. The role requires expertise in deep learning, model optimization, self-supervised learning, robustness, explainability, or RLHF.

Develop and deploy foundational AI systems, including foundation model training, LLM inference, agent workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for scalability, cost, latency, throughput, and hardware utilization. Build training and deployment pipelines, improve datasets and evaluation methods, support responsible AI practices, and mentor junior engineers while contributing to long-term technical strategy.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across production systems. Own complex AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews, collaborate across engineering and product teams, and mentor senior and principal associates.

Build and deploy machine learning models and scoring platforms for credit decisioning and valuation. Analyze large-scale financial and textual datasets using statistical modeling, cloud technologies, and open-source tools. Responsibilities include model design, training, validation, implementation, analytical tool development, alternative data analysis, and collaboration with software engineering and product teams to deliver scalable credit products.

Design, develop, deploy, optimize, and support foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, design reviews, and technical roadmaps for scalable production AI. Optimize model performance, hardware utilization, latency, throughput, and cost while ensuring responsible AI standards. Mentor senior technical staff and collaborate with engineers, researchers, program managers, and product managers.

Design, develop, deploy, optimize, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture and cost-performance optimization across scalable AI platforms, establish engineering standards, contribute to long-term technical roadmaps, and mentor senior AI engineers.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and multi-model orchestration. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform strategy. Apply state-of-the-art optimization techniques to improve model scalability, latency, throughput, hardware utilization, and cost. Mentor senior technical leaders and collaborate with engineers, scientists, product managers, and program managers.