Top AI Research Jobs in Boston, MA

Conduct independent AI research for predictive toxicology and drug safety. Develop novel deep learning architectures, multimodal models, representations, uncertainty methods, and mechanistic reasoning approaches for heterogeneous biological and chemical data. Design rigorous experiments, benchmarks, ablations, and generalization evaluations while collaborating with engineering and scientific teams to translate validated research into scalable modeling systems. Contribute to scientific strategy, publications, collaborations, and regulatory validation.

Conduct applied research and develop scalable systems for dynamic context graphs, knowledge representation, graph machine learning, information extraction, retrieval, and LLM-based reasoning. Build methods for entity and relationship resolution, temporal reasoning, provenance, uncertainty, and agentic workflows. Design evaluation benchmarks for graph quality and reasoning reliability, prototype systems with engineering teams, and contribute through publications, patents, technical strategy, and mentorship.

Conduct research developing multimodal AI and machine learning systems for biomedical discovery and drug development. Integrate imaging, omics, molecular, literature, and clinical data to generate biological insights, identify disease mechanisms, and prioritize targets or compounds. Responsibilities include model development, benchmarking, scientific software creation, experimental collaboration, documentation, publications, and communication of findings across multidisciplinary teams.

Research enterprise AI use cases through interviews with partners, solution architects, and field sales teams. Analyze qualitative and quantitative findings, identify AI adoption opportunities, map customer needs to Red Hat’s AI platform and partner ecosystem, and recommend partner engagement strategies. Present a final report with strategic recommendations to AI Ecosystem leadership.

Conduct research on large-scale learned humanoid manipulation and robotics foundation models. Develop architectures, learning objectives, reinforcement and imitation learning methods, multimodal behavior models, and cross-embodiment transfer techniques. Design rigorous benchmarks and real-world experiments, train and deploy models on physical humanoid robots, analyze failures, and translate findings into scalable systems. Provide technical leadership, mentor colleagues, and communicate results through reviews, publications, presentations, and open-source work.

Develop and research novel machine learning algorithms for brain-computer interface applications. Improve brain activity identification, speed, accuracy, and muscle-artifact removal using real-time and large EEG datasets. Responsibilities include implementing and creating neural-network and other machine learning methods, rapidly prototyping features, collaborating with developers and the CEO, advancing intellectual property, documenting work, and contributing creatively in a fast-paced startup environment.

Conduct research advancing healthcare AI, including model evaluation, clinical benchmarks, human assessments, health-focused model development, agentic systems, prototyping, and simulations. The role involves pre-training and post-training workflows, research publications, collaboration with clinical and engineering teams, and sharing research practices. Candidates will apply expertise in machine learning, natural language processing, clinical data, and healthcare data standards such as FHIR and OMOP.

Design, develop, and deploy machine learning models for indoor positioning and perception. Optimize model architectures, create real-world benchmarking tools and 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 and probabilistic models, production coding, and research publication, with opportunities to apply computer vision, wireless localization, sensor fusion, and mobile ML deployment.

The role involves leading AI and advanced analytics initiatives in life sciences, managing client relationships and P&L, developing strategies for growth, and contributing to the team's thought leadership while enhancing analytical models and solutions.

Develop and implement quantum error correction protocols and decoding algorithms, collaborating with academic and industry partners on a Rydberg atom quantum computer.