Machine Learning Jobs in Cambridge, MA

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Lead design and implementation of AI and blockchain-enabled integration and platform architectures. Analyze requirements, develop technical designs, direct large projects, validate outcomes with clients, coach diverse teams, drive cloud strategy (Azure), perform feasibility studies and business process modeling to optimize enterprise technology infrastructure.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Leads data science initiatives for US Card acquisitions, developing and deploying machine learning models across their full lifecycle. Partners with data scientists, software engineers, product managers, and business stakeholders to solve complex problems, analyze large-scale numeric and textual datasets, validate models, and translate technical findings into business outcomes. The role also involves team leadership, talent development, model governance, monitoring, and ongoing model improvements.

Supports data science teams by cleaning and processing structured and unstructured data, engineering features, executing and monitoring machine learning pipelines, generating insights, investigating trends and risks, and collaborating with senior data scientists and business analysts. The role requires a current MIT quantitative-field bachelor's student working 10–20 hours weekly in the continental United States.

Develops AI and machine learning prototypes for insurance applications using Python, large language models, NLP, retrieval-augmented generation, agentic AI, information retrieval, and computer vision. Collaborates with data scientists and engineers on ideation, product design, algorithm development, data analysis, and experimentation in an R&D environment.

Leads and scales a human subjects research team supporting early-stage innovation, hardware, wearables, apparel, sensors, algorithms, and other physical products. The role manages researchers, promotes research integrity, monitors KPIs, coaches cross-functional teams, and collaborates with engineering, machine learning, product, privacy, recruitment, and global lab operations leaders.

Develop agentic AI, predictive models, and MLOps capabilities for pharmaceutical drug discovery. Build scalable chemical database search and analytics systems, apply cheminformatics and computational chemistry across the DMTA cycle, and explore generative chemistry, synthesizable chemical spaces, and DEL data analysis. Collaborate with medicinal chemistry, DMPK, and AI/ML teams to establish computational tool strategies and accelerate molecule design and optimization.

Build, deploy, scale, and maintain machine learning models and infrastructure for production use. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software development practices. The role collaborates with Product, Data Science, and Agile engineering teams.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Design, develop, secure, modernize, and support scalable omnichannel APIs for customer-facing web, mobile, contact center, partner, and enterprise applications. Improve API security, resiliency, performance, observability, testing, and reliability. Collaborate with architecture, security, cloud, product, operations, and engineering teams; lead technical delivery, mentor engineers, develop reusable patterns, and provide Tier 3 production support. Apply AI-assisted engineering practices and contribute to API lifecycle management and continuous improvement.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

Design and implement Dynamics 365 Finance solutions supporting financial operations, integrations, reporting, automation, and AI initiatives. Optimize General Ledger, accounts payable, accounts receivable, cash management, fixed assets, and project accounting processes. Lead finance system projects, stakeholder engagement, audit and reporting improvements, training, mentorship, and M&A-related activities. Advise finance leadership on ERP capabilities, Microsoft Copilot, Power Platform, data governance, and emerging AI technologies.

Design, implement, debug, and refine advanced neuro-symbolic AI and machine learning systems. Collaborate with researchers, engineers, and subject-matter experts to develop deployable solutions for national security and commercial applications. Responsibilities include leading R&D programs, managing technical collaborations, writing reports and papers, engaging customers, and advancing deep learning, probabilistic reasoning, symbolic knowledge, optimization, and related technologies from research through deployment.

Senior Scientist will lead protein library design, construction, display, selection, sorting, and genotype-to-phenotype analysis for a biotech drug-discovery platform. Responsibilities include deep mutational scanning, FACS workflow development, library quality assessment, protein characterization, experimental data interpretation, documentation, and collaboration with computational and screening teams. The role also sets technical direction, builds the protein-engineering function, and helps shape scientific strategy as an early startup hire.

Leads the strategic application of medical imaging across Roche’s cardiovascular, renal, and metabolism drug development pipeline. Defines scalable, regulatory-ready imaging endpoints and roadmaps; applies AI, machine learning, and advanced analytics; oversees imaging algorithms from assessment through deployment; and drives global cross-functional initiatives, governance, partnerships, and data standardization.

Leads imaging science strategy for Roche’s Cardiovascular, Renal & Metabolism drug development portfolio. The role defines scalable, regulatory-ready imaging endpoints; integrates AI, machine learning, and advanced analytics; directs imaging algorithms from assessment through deployment; and leads global, cross-functional initiatives. Responsibilities include roadmap development, governance, stakeholder influence, clinical trial support, external collaboration, and standardization of imaging data assets.

Build and productionize data and AI applications on Palantir Foundry for biomedical domain teams. Responsibilities include designing data pipelines, transformations, ontology models, workflow applications, integrations, access controls, data quality checks, and production monitoring. The role leads discovery and iterative delivery, partners with product, data, governance, and platform teams, supports user adoption, and creates reusable delivery patterns. Candidates need strong programming, data and application engineering, stakeholder management, and biomedical domain experience.

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.

Leads enterprise data science initiatives for financial crime risk modeling and advanced analytics. Develops and enhances AML/CTF models, customer rating, sanctions screening, transaction monitoring, anomaly detection, and model performance monitoring solutions. Translates business problems into scalable data science solutions using Python, SQL, statistics, machine learning, and GenAI. Communicates insights to stakeholders, provides technical leadership, supports regulatory compliance, and mentors team members.

Own the strategy and roadmap for WHOOP’s foundational sensing algorithms. Lead cross-functional work across product, sensor intelligence, machine learning, hardware, embedded systems, and science to improve measurement accuracy, reliability, robustness, and performance. Use field data, validation, member feedback, and benchmarking to identify opportunities, define success criteria, make technical tradeoffs, and translate sensing advances into improved health and performance experiences.

Senior Patent Counsel will develop and manage global patent portfolios for protein therapeutics and platform technologies. Responsibilities include identifying inventions, advising on patent and trade-secret strategies, prosecuting applications, conducting patentability and freedom-to-operate analyses, supporting licensing and collaboration transactions, reviewing disclosures, managing outside counsel and budgets, and advising scientists and executives on intellectual property matters.