Top Machine Learning Jobs in Boston, MA

As an AI Research Scientist II, you will develop and prototype AI solutions, advance drug design campaigns, and publish research while collaborating cross-functionally on cheminformatics and bioinformatics.

Lead product strategy for Suno's research org by defining evaluation frameworks, connecting creator needs to model capabilities, prioritizing research, running experiments, and bringing model improvements to market in partnership with researchers and cross-functional teams.

Lead research and advisory work on data foundations for analytics and ML: produce 8–12 research assets annually, drive vendor evaluations (Forrester Wave), consult clients on data/AI strategy, present at events, support business development, and foster cross-team collaboration. Focus areas include data quality, governance, context-ready data, large-scale data management, and measuring business outcomes from analytics and ML.

Lead cross-functional ML/AI initiatives, drive technical roadmaps, mentor teams, and deliver impactful ML/AI solutions for business growth.

The Director & Group Head of Applied AI will lead AI strategy in drug discovery, collaborating with teams to deploy machine learning solutions and drive innovation in biomedical research.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

Lead design, development, and implementation of advanced ML and NLP models for payment integrity. Translate business problems into analytics, run large-scale experiments, automate analyses, validate and operationalize models, and present actionable insights. Manage multiple projects, mentor junior staff, and enforce governance, ethical norms, and scalable processes to improve quality, member experience, and cost outcomes.

Lead and manage a software engineering team building robotics and AI solutions. Oversee sprints, performance, platform availability, demos, deployments, manufacturing guidance, and cross-team coordination. Maintain deep technical understanding to contribute to troubleshooting, commissioning, and fielded product success.

Lead enterprise-level strategy and delivery for building design, engineering, data readiness, and AI adoption. Define customer success plans, run workshops, shape consulting offerings, mentor staff, drive change management, and ensure high-quality project delivery and measurable customer value.

Lead the Oncology Data Science team in developing data-driven solutions for low molecular weight drug development, leveraging AI and machine learning. Collaborate with stakeholders to execute data science strategies and ensure adherence to best practices in analytics.

Lead enterprise applied AI strategy and a distributed team to design, deploy, and scale generative AI and agentic solutions. Drive solution architecture, integrations, MLOps/LLMOps, reusable services, governance, and cross-functional adoption while measuring business impact and modernizing systems for production use.

Lead an Agile team to design, build, deploy, and maintain production-scale ML models and pipelines. Drive ML architecture, model training, CI/CD, monitoring, and cloud/Kubernetes-based deployments while ensuring responsible, explainable, and well-governed ML solutions.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Lead and manage AI-focused consulting engagements: define hypotheses, oversee analytics and data collection, translate client needs into technical requirements, support ML/generative AI solution delivery from proof-of-concept to production, build executive relationships, and coach consultants to deliver measurable business impact.

Lead the Accounting & Finance Reporting, Data & AI Center of Excellence: set strategy, governance, roadmap, and deliver analytics, automation, and AI solutions. Own intake, prioritization, implementation, adoption, and value realization across financial processes while partnering with IT and mentoring the team.

Lead client-facing, end-to-end AI strategy engagements: scope opportunities, design solutions, build roadmaps, and oversee implementation. Advise C-suite on AI adoption, value realization, governance, and technical enablement. Maintain hands-on fluency with modern AI stack and platforms, drive business development, mentor consulting teams, and contribute to an AI Center of Excellence.

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

Seeking a Senior AI Engineer to develop AI solutions, oversee project delivery, and collaborate on system architecture. Requires 5+ years with Python and AI technologies.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

As an AI Product Specialist at Kensho, you will drive market expansion, manage customer engagement, and leverage AI product strategies while collaborating across teams to ensure effective go-to-market execution for AI solutions.