Top AI Research Jobs in Boston, MA

Leads research advancing AI models and agents for cybersecurity, including reasoning, tool use, vulnerability discovery, threat detection, malware analysis, incident investigation, and automated defense. Defines research agendas, develops and evaluates prototypes in realistic security environments, improves robustness and explainability, collaborates across research and engineering teams, publishes findings, mentors researchers, and translates foundational advances into deployed security capabilities.

Own product backlog and roadmap for knowledge and semantic platform capabilities (ontology, knowledge graph, taxonomy, metadata). Conduct discovery with internal clients, define semantic models, entity relationships, and requirements; partner with engineering, data, and AI teams to deliver reusable platform services; promote governance, data stewardship, validation, adoption, and metrics to improve semantic quality and AI-enabled product delivery.

Leads Year Up United’s AI innovation portfolio, maintaining visibility into initiatives, managing intake and governance, running low-cost pilots, and making evidence-based go/kill decisions. The role evaluates emerging AI capabilities, builds organizational AI fluency through communities and Champions, partners cross-functionally, and hands validated pilots to technical teams for scaling. It also includes mentoring students and occasional travel to organizational sites.

Manages interdisciplinary AI research projects spanning model evaluation, interpretability, research infrastructure, technology policy, trust and safety, and AI accountability. Coordinates collaborators, workflows, deliverables, grants, publications, and stakeholder events. Supervises junior researchers and supports technical and policy-oriented research outputs, including academic papers, policy briefs, essays, and presentations. The role also requires machine learning experimentation on GPU clusters, research reproducibility, proposal development, and engagement with policymakers, industry, academia, and civil society.

Conduct computational research in knowledge-grounded AI, knowledge graphs, systems modeling, natural language processing, machine learning, and biomedical data harmonization. Develop open-source scientific software and benchmark applications, publish and present research, contribute to grant proposals, supervise graduate students and junior researchers, and support lab purchasing and budget administration.

Design, develop, optimize, and deploy machine learning models for indoor positioning and perception. Build benchmarking tools and real-world datasets, translate research into production pipelines, and collaborate with engineering and product teams to ship enterprise features. The role requires advanced expertise in time-series machine learning, transformer architectures, probabilistic models, and production software development, with opportunities to apply computer vision, sensor fusion, localization, and mobile ML optimization.

Lead AstraZeneca’s AI research agenda for clinical development across oncology and biopharmaceuticals. Develop methods for treatment-effect heterogeneity, biomarker discovery, patient stratification, survival analysis, and trial generalization. Design and evaluate agentic AI systems using Python and modern machine-learning frameworks. Establish reproducible engineering, responsible AI governance, and regulatory-aligned evaluation practices while influencing clinical strategy and mentoring scientists and engineers. Disseminate findings through publications, patents, partnerships, and conferences.

Lead research into emerging AI methods, design rigorous experiments and evaluations, develop novel modeling and agentic approaches, and translate findings into reusable, production-minded enterprise AI capabilities. The role requires hands-on Python development, collaboration with engineers, communication across technical and business audiences, and mentoring colleagues without direct reports. Travel of up to 10% may be required.

Conduct multimodal AI and computer vision research on skin and hair perception. Develop and evaluate Generative AI approaches, connect image-based measurements with consumer data, build evaluation datasets and perception-alignment metrics, explore agentic AI workflows, and translate findings into actionable R&D and product-development insights.

As a Research Engineer/Scientist in Reinforcement Learning, you will develop RL methods, maintain experimental infrastructure, and collaborate to enhance AI solutions in critical sectors.