AI Research Scientist

• Develop AI/ML models to predict structural biomolecular interactions for novel modalities leveraging protein-protein interactions.
• Design innovative AI/ML approaches to predict protein cooperativity, affinity & other biological properties based on structures.
• Develop co-folding models that incorporate structural and non-structural priors using combinations of public and proprietary BMS data.
• Develop AI/ML models that leverage chemoproteomics data contributing to the generation of novel druggability hypotheses for hard-to-drug targets.
• Author scientific reports, and present methods, results, and conclusions to publishable standard.
• Contribute to the planning and execution of collaborative projects with leading academic and commercial research groups worldwide.

Senior Scientist, AI Computational Structural Biology

• Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration.
• Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks.
• Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation.
• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities.
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence, such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses.
• Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence.
• Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning.
• Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions.
• Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives.
• Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS).
• Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery.
• Co-author manuscripts, concept papers, and translational strategy documents.

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

• Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration
• Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks
• Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation
• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses
• Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence
• Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning
• Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions
• Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives
• Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS)
• Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery
• Co-author manuscripts, concept papers, and translational strategy documents

• Design, develop, and deploy advanced generative AI workflows and multi-agent conversational architectures (e.g., using LangGraph) to power personalized user interactions and automated symptom triage
• Implement, build on, and augment existing LLM/NLP tools to automate the abstraction of high-priority clinical variables and derived features from unstructured medical text, maximizing data completeness and accuracy
• Develop automated evaluation pipelines and 'LLM-as-a-judge' grading rubrics to continuously benchmark agent safety, accuracy, factuality, and compliance across model updates, enabling self-optimization cycles
• Handle real-world data challenges from clinical and remote settings, ensuring absolute safe data boundaries, privacy preservation, and rigorous technical validation before production releases
• Communicate highly technical results, methods, and evaluation frameworks clearly via presentations and well-structured reports to both technical and non-technical cross-functional audiences

Staff AI Applied Scientist

• Conduct fundamental and innovative development in low-cost yet powerful vision-language models (VLM), unified models, automatic model compression, optimization and deployment on cloud and edge.
• Design or implement state-of-the-art techs on model compression, inference speedup, deployment on hardwares, tool automation.
• PoC for various vision+text, generation relevant tasks (VQA, captioning, understanding, etc) and hardwares.
• Contribute to library and tool development to support business; or Publish influential research in top-tier conferences and journals.

Research Intern, Vision Foundation Model and Generative AI

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Architect, train, fine-tune, and evaluate state-of-the-art speech representations and ASR models (e.g., End-to-End Conformer, Whisper, RNN-T, and hybrid CTC/Attention architectures) across multiple global languages
• Design and deploy highly scalable algorithms for dynamic vocabulary insertion, contextual biasing, and language model (LM) personalization to precisely capture customer-specific terminology, acronyms, and product names
• Implement automated framework evaluations to benchmark model performance, rigorously tracking Word Error Rate (WER), Character Error Rate (CER), embedding-based metrics, latency budgets (RTF), and computing efficiency profiles under varying acoustic environments
• Develop pioneering multilingual benchmarks for end-to-end conversational AI agents, including speech-to-text and text-to-speech components
• Partner with core engineering teams to build, optimize, and maintain high-throughput pipelines optimized for both ultra-low latency real-time streaming inference and high-efficiency asynchronous (batch) multi-channel speech analysis
• Translate product requirements into technical AI roadmaps, working hand-in-hand with Product Managers to ship speech-to-text, simultaneous translation, and semantic speech analytics features

AI Research Scientist jobs in Massachusetts

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Senior Scientist, AI Computational Structural Biology

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Staff AI Applied Scientist

Research Intern, Vision Foundation Model and Generative AI

AI Research Scientist, Applied AI

AI Researcher / ML Engineer – ASR, Speech Specialist