Top AI Jobs in Boston, MA

Lead and deliver large-scale AI/ML programs and the next-generation Generative AI platform. Build and grow a world-class TPM discipline, manage cross-functional delivery, mitigate technical risk, and drive execution across product, engineering, design, and data science to achieve business impact in regulated environments.

The Enterprise Architect will design scalable AI solutions, mentor teams, support business development, and manage client relationships while evolving technology practices.

Lead program design and delivery for DTC data and AI initiatives: manage Snowflake-based data pipelines, build and deploy AI tools, design processes, ensure measurement governance (GA4), and align stakeholders to drive commercial growth.

Lead technical vision and execution for AI/ML, decision systems, and mission planning programs. Manage multidisciplinary teams, ensure schedule and budget adherence, provide technical oversight, cultivate external partnerships, and pursue business growth while guiding program delivery and technical strategy.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

Lead data science projects to advance drug development, implementing models for patient selection and clinical trial optimization, and collaborating with cross-functional teams.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

As AI Enablement Services Lead, you will design AI solutions for operational tasks, automation of delivery processes, and enhance team capabilities in utilizing AI effectively.

The People Systems Developer designs and implements AI-native workflows for HR processes, focusing on talent acquisition and performance improvement through automation and integration of systems.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Lead a cross-organizational program to transition engineering to an AI-driven, self-optimizing ecosystem with robust telemetry and evaluation. Orchestrate strategy and execution, mitigate technical risk, build and grow a TPM practice, align product/engineering/design/data science, and deliver measurable business impact.

Lead the design of biotherapeutics using AI/ML methods. Collaborate with teams to implement workflows, optimize antibody engineering, and analyze large biological datasets.

Lead design and implementation of AI and blockchain-enabled integration and platform architectures. Analyze requirements, develop technical designs, direct large projects, validate outcomes with clients, coach diverse teams, drive cloud strategy (Azure), perform feasibility studies and business process modeling to optimize enterprise technology infrastructure.

Lead design, launch, and lifecycle management of Pluralsight solutions (AI, Cloud, Security). Define expert POV, build curricula, labs, assessments, and blended learning programs. Own roadmap, metrics, GTM partnership with Sales and Marketing, and continuous iteration using data and SME collaboration to drive enterprise customer outcomes.

Lead and grow an AI Platform Enablement team to build a governed internal AI development environment. Define platform strategy, partner with security and infrastructure, drive adoption via enablement programs, and establish metrics for usage, onboarding, and control effectiveness. Act as an executive-facing leader delivering secure, scalable platforms and change management.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

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.

Conduct applied research to design, train, evaluate, and deploy AI foundation models and recommendation/personalization systems. Collaborate with cross-functional teams to build models at scale using PyTorch, cloud infrastructure, and open-source tools. Publish research, deliver platform-level code, and drive long-term research agendas to improve customer experiences.

Lead company-wide AI transformation from fluency to measurable business impact. Build agentic workflows and enablement, coach leaders and teams, advise the CEO, manage budget and platform economics, define and grow the AI function, and run the operating rhythm that drives adoption and scale across the organization.

Lead and manage cross-firm AI in Operations program initiatives: develop strategic plans, apply structured problem-solving and design thinking, support multiple workstreams, engage senior stakeholders, standardize AI use cases, and drive scaling and reimagination of AI capabilities across operations.

Lead firmwide AI transformation by defining portfolio strategy, prioritizing high-impact AI initiatives, building governance and measurement frameworks, advising senior leaders with data-driven narratives, clearing execution roadblocks, and managing a team to deliver measurable strategic and financial value.

Design and prototype AI-powered solutions (LLMs, agents, prompts) to solve creative and operational problems, integrate AI with internal data and APIs, enable teams through documentation and training, and partner with engineering to productionize scalable workflows across Services.

Design and prototype AI-powered solutions for Customer Success using LLMs and automation. Translate business needs into roadmaps, build prompts/agents/ retrieval workflows, integrate APIs and internal data, enable teams with documentation and training, and partner with engineering to productionize scalable AI systems.

Design and implement AI systems and scalable ML models (Python, TensorFlow); build/maintain data pipelines and integrations; apply NLP (NLTK) and deep learning for analytics; deploy and optimize models (including open-weight LLMs); collaborate with stakeholders and continuously learn to improve AI solutions and deployment reliability.

Lead design and build of an AWS-only AI Operations platform: hosting, IaC, registries (Accountability, Agent, Tool), identity/authorization patterns, developer DX, observability, and security hooks for agentic systems. Evangelize internally and partner on compliance and security integrations.