Top AI Research Jobs in Boston, MA

Conduct applied AI research focused on foundation models, evaluation, and agentic workflows. Build, train, validate, and deploy large-scale deep learning models using technologies such as PyTorch, AWS, Hugging Face, and Lightning. Lead research initiatives, develop platform and solution-level code, scale models for production, collaborate with engineering and product teams, and translate research findings into customer-facing AI products.

Develop and evaluate advanced machine learning methods for biomedical, clinical, molecular, and scientific data. Build foundation models, multimodal and graph-based systems, generative methods, and agentic reasoning tools. Design rigorous experiments, benchmarks, and validation frameworks; assess robustness, uncertainty, interpretability, and scientific utility. Collaborate with researchers, engineers, computational biologists, and domain experts to translate research into reusable capabilities supporting drug discovery and development.

Research and develop real-time AI algorithms for audio source separation, target sound extraction, and speech enhancement. Work with single- and multi-microphone recordings, spatial cues, auxiliary sensors, and challenging acoustic conditions. Train and evaluate PyTorch models while assessing perceptual quality, latency, memory, and computational efficiency. Collaborate with DSP and embedded software teams to deploy models on physical devices and contribute to patents and research publications.

Research, design, train, and deploy multimodal AI models using clinical and molecular data to support precision medicine, biomarker discovery, therapy outcome prediction, and companion diagnostics. The role requires collaboration with life sciences companies, regulatory alignment for healthcare products, and publication of original research. Responsibilities include working with pathology slides, genomic profiles, spatial omics, and longitudinal patient records while contributing hands-on model development and product-level engineering.

Conduct fundamental research on on-device LLM efficiency and optimization. Develop proposals, survey academic literature, formulate hypotheses, and run experiments involving quantization, fine-tuning, compression, decoding, and model architectures. Collaborate with internal teams and academic labs, transition prototypes into production, publish at leading AI conferences, and independently drive research initiatives from ideation through implementation.

Leads multidisciplinary teams developing, modeling, integrating, testing, and demonstrating signal processing, optimization, and machine learning algorithms for radar, electronic warfare, and advanced sensor systems. Responsibilities include developing RF and radar concepts, directing modeling and simulation, supporting laboratory and field test evaluations, assessing system performance, translating customer requirements into research priorities, and presenting technical solutions to customers, sponsors, and senior management.

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.

Designs and prototypes scientific evaluations and reinforcement learning environments for frontier language models. Builds scalable pipelines that transform scientific assets into validated training environments, develops reward shaping and data collection workflows, and integrates experiments into model training infrastructure. Requires applied ML or computational experience in biology, chemistry, or materials science, strong Python skills, data pipeline expertise, research creativity, and software engineering fundamentals. Experience with LLM training, distributed computing, inference systems, and workflow orchestration is advantageous.

Designs reinforcement learning environments, multi-agent harnesses, and synthetic environment-generation pipelines for AI research. Responsibilities include prototyping tool interfaces, planning mechanisms, agent swarms, reward shaping, data collection, evaluation scenarios, and integration with training infrastructure. The role requires strong Python coding, reinforcement learning knowledge, research creativity, and software engineering fundamentals. Preferred experience includes scientific domains, developer-facing frameworks, distributed compute, LLM inference, and workflow orchestration.

Lead the AI x Lab Integration research agenda, developing models that understand scientific protocols, plan and execute experiments, diagnose failures, and adapt based on results. Recruit and mentor research scientists and engineers, establish rigorous evaluations, collaborate with experimental scientists and laboratory teams, and guide research in post-training, reinforcement learning, tool use, and learning from execution feedback.

Conduct interdisciplinary AI and human systems research with Microsoft Research mentors and fellow doctoral interns. Contribute to studies involving AI evaluation, human-AI interaction, societal impacts, agentic systems, markets, and institutions. Collaborate with researchers, present findings, develop original research, and contribute to the research community during a 12-week internship.

Conduct research adapting pretrained world foundation models into robot policies for long-horizon tasks. Train and fine-tune models, evaluate them in simulation and on humanoid or mobile-manipulator robots, design task and human-interaction evaluations, collaborate with robotics researchers and engineers, and communicate results through presentations, publications, and open-source releases.