Top AI Research Jobs in Boston, MA

Conduct applied AI research and build foundation models through design, training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research large deep learning models, optimization, self-supervised learning, robustness, explainability, and fine-tuning, while translating technical findings into business outcomes and pursuing an independent research agenda.

Lead the technical direction and execution of WHOOP’s foundation AI modeling efforts across wearable sensor data, language, biomarkers, and behavioral signals. Drive high-risk research from hypothesis to production, shape AI strategy and infrastructure decisions, collaborate across research, product, and engineering, mentor senior technical talent, establish evaluation standards, and represent WHOOP externally through recruiting, partnerships, and the machine learning research community.

The Associate Principal Scientist will design and execute research projects, perform computational analyses, and communicate insights in precision medicine for oncology, collaborating with stakeholders.

Senior technical leader defining 10-year scientific visions and systems architecture for Bose Research franchises. Ensures coherence across AI/ML, sensing, signal processing, and edge domains; guides foundational IP, models, datasets, and evaluation frameworks. Oversees program architecture, technical governance, and translation of research into scalable platforms and products while representing Bose externally and collaborating with research and engineering stakeholders.

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

The Lead Research Scientist develops advanced AI/ML technologies for signals exploitation and system management while leading project teams and collaborating with customers.

Own Deepgram’s end-to-end text-to-speech research program, including technical strategy, model architectures, experiments, training, evaluation, and production deployment. Lead researchers and technical managers, establish roadmaps, advance naturalness, expressiveness, multilingual generation, controllability, voice consistency, and inference efficiency, and partner with engineering and product leadership to ship high-quality models.

Manage and coach a team of mentors to ensure delivery outcomes for ~200 AI fellows at scale. Build delivery systems, run operational cadence, lead live instruction when needed, translate field signals into curriculum changes, and partner with strategic partners to close the feedback loop between mentors, fellows, and the curriculum team.

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.