Top AI Research Jobs in Boston, MA

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

Develop evolutionary algorithms for autonomous scientific discovery, enabling populations of models, hypotheses, programs, and representations to evolve through iterative search, selection, evaluation, and recombination. Build Bayesian and probabilistic methods for candidate ranking, uncertainty estimation, diversity preservation, and quality measurement. Establish benchmarks, integrate algorithms into autonomous-science pipelines, publish research, guide technical direction, and mentor colleagues as a senior individual contributor.

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.

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.

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.

The Scientist in Platform and TA Bioinformatics will design mRNA sequences, improve computational methods, handle biological datasets, and develop tools for sequence workflows.