Top AI & Machine Learning Jobs in Boston, MA

Design, develop, deploy, and support foundational AI systems, including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, scalability, throughput, and hardware utilization. Own complex system architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term AI roadmaps, and mentor engineering talent.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, machine learning engineers, and product managers to deliver AI-powered customer experiences. Research emerging methods, develop scalable model and platform code, translate technical findings into business outcomes, and contribute to technical reports or conference publications. The role requires ownership of a research agenda and expertise in deep learning, optimization, robustness, explainability, or model fine-tuning.

Build and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and orchestration pipelines. Lead cost-performance governance, improve model scalability and hardware utilization, shape technical roadmaps, establish AI engineering standards, and mentor senior technical staff. The role requires applying current AI research to reliable, responsible production systems across cloud and large-scale infrastructure.

Leads an AI engineering team developing, deploying, and supporting production AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, observability, and responsible AI governance. Owns technical strategy, optimization, build-versus-buy decisions, roadmaps, team development, and cross-functional collaboration. The role also applies emerging AI research to scalable, reliable, cost-effective production systems.

Conduct applied research and develop large-scale AI foundation models, particularly LLMs, optimization methods, and fine-tuning techniques. Responsibilities include designing, training, evaluating, validating, and deploying models; optimizing training and inference at scale; collaborating with data scientists, engineers, and product managers; translating research into business applications; and producing technical reports or conference publications.

Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, software engineers, machine learning engineers, and product managers to develop scalable AI products. Lead research initiatives, optimize training and inference, evaluate emerging methods, translate research into business outcomes, and deliver model libraries or platform code. Work may focus on large language models, deep learning optimization, fine-tuning, robustness, explainability, or reinforcement learning.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture, cost-performance optimization, technical standards, and design reviews for scalable production AI systems. Mentor senior technical staff and contribute to the long-term roadmap for responsible AI infrastructure.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and observability. Lead architecture, cost-performance governance, AI standards, and design reviews while mentoring senior technical staff. Partner with research, engineering, product, and program teams to establish foundational AI roadmaps and deliver scalable, responsible systems.

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Research LLM pretraining, fine-tuning, reinforcement learning, robustness, explainability, and behavioral models. Develop platform or solution-level code, reproduce research, document experiments, and contribute to technical reports or conference submissions.