Top Machine Learning Jobs in Boston, MA

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Leads a marketing data science team developing and deploying machine learning models for customer acquisition and personalization. Responsibilities include designing, training, validating, evaluating, and implementing models; analyzing large-scale structured and textual data; developing Python libraries; collaborating with engineers, product managers, campaign managers, and analysts; and translating technical insights into business outcomes. The role also includes team leadership and talent development in a hybrid work environment.

Develop, fine-tune, train, and optimize computer vision and machine learning algorithms. Manage experiments, data, testing, validation, and reproducibility using MLOps tools. Research emerging ML techniques, analyze field-test data, and transition algorithms into production across cloud, on-premises, and embedded environments. Collaborate with engineers and national security customers to deliver mission-critical AI and natural language processing capabilities.

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.

Leads the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable ML serving infrastructure. Establishes engineering standards, drives complex initiatives, oversees reliability and cloud cost optimization, and partners across engineering and data teams. The role also mentors engineers, advances automation, supports incident response, and applies AI-assisted development practices.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

Develop and optimize deep learning algorithms for resource-constrained embedded audio devices. Build software prototypes, deploy models, research efficient techniques such as quantization and pruning, evaluate models, and assess neural-network hardware accelerators. The role focuses on audio ML for headphones, earbuds, speakers, soundbars, and wearables, requiring embedded C/C++, Python, deep learning frameworks, and collaboration across teams.

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.

Leads Voya’s enterprise AI, automation, intelligence, and data strategy and operations. Builds scalable AI and agentic platforms, embeds AI across software development, industrializes machine learning and data science, advances intelligent automation, and establishes enterprise data and knowledge architecture. The executive also drives responsible AI governance, security, adoption, measurable business outcomes, and high-performing distributed teams across AI engineering, platform engineering, data science, automation, and data.

Owns end-to-end architecture and system design for an enterprise GenAI platform and shared AI services. Responsibilities include defining reference architectures, integration contracts, and non-functional requirements; designing scalable cloud-based AI systems; establishing patterns for RAG, model serving, prompt workflows, and agentic AI; ensuring security, governance, compliance, resiliency, and observability; and leading architecture reviews, proof-of-concepts, and production-readiness planning across engineering, product, data science, cloud, and control teams.

Leads enterprise-wide data and AI governance strategy, including policies, standards, stewardship, data quality, analytics platforms, responsible AI, security, privacy, compliance, and model lifecycle management. Builds and manages governance, data science, and analytics teams while partnering with IT, legal, compliance, and business leaders. Evaluates governance technologies, advances data-driven decision-making, and promotes ethical, secure, transparent, and accountable use of data and AI.

Leads and develops a team of machine learning, backend, and data engineers building recommendation and personalization systems. The role guides technical strategy, production ML development, experimentation, scalability, reliability, and model performance while aligning priorities with Product and engineering partners. It also drives planning, delivery, coaching, inclusive team culture, and long-term technical health for systems serving millions of Spotify listeners.

Develop and refine machine learning and LLM-powered models for clinical use cases, from prototyping through production. Conduct rigorous evaluation and error analysis, identify failure modes, implement targeted improvements, and ship frequent model updates. Apply prompting, structured outputs, tool use, and evaluation frameworks while contributing to performance metrics, reporting, and dashboards. Collaborate closely with the ML team in a fast-paced, high-ownership environment.

Develop and deploy scalable, secure AI and machine learning solutions for business applications. Responsibilities include preparing datasets, building and evaluating models, monitoring deployed systems, optimizing performance, researching emerging AI technologies, and integrating AI capabilities with existing workflows. The role collaborates with data scientists, software engineers, and business stakeholders to deliver intelligent systems that improve efficiency and decision-making.

Develops and enhances AML/CTF risk models and AI solutions for financial crime compliance. Applies statistics, machine learning, anomaly detection, Python, SQL, and GenAI techniques to complex data. Partners with business, engineering, and analytics teams to translate business problems into scalable solutions, communicate insights, monitor model performance, address risks, and provide technical leadership across enterprise initiatives.

Develop agentic workflows and automation for real-time audio machine learning research. Build systems for data preparation, model training, hyperparameter optimization, evaluation, regression testing, experiment tracking, artifact management, and reporting. Operate GPU infrastructure and connect LLM agents to research tools, datasets, repositories, and compute resources. Collaborate with audio researchers to create reliable, reproducible workflows and observable dashboards.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Analyze autonomous vehicle, fleet, and business data to develop metrics, evaluate model and simulation performance, identify behavioral patterns, and support data-driven decisions. Projects may include drift monitoring, automated retraining, predictive modeling, similarity search, and statistical testing. The intern will use Python and SQL, apply machine learning and analytical methods, collaborate with simulation and autonomy teams, and communicate findings to varied audiences.

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.

Leads Sanofi’s agentic AI strategy for vaccine computational design. The role develops and deploys AI agents, machine learning models, and large language model workflows across bioinformatics, nucleic acid design, protein modeling, viral surveillance, and translational data science. Responsibilities include establishing engineering, governance, validation, and reproducibility standards; collaborating across R&D, Digital, CMC, and Clinical teams; industrializing AI-assisted workflows; and mentoring scientists across the organization.

Leads applied machine learning and statistical research for health features using longitudinal wearable, physiological, behavioral, and multimodal data. Designs studies, models, evaluation protocols, and quality standards; partners with engineers to productionize methods; advises product, clinical, regulatory, and digital health teams; mentors scientists and engineers; and communicates evidence, limitations, risks, and trade-offs to technical and executive stakeholders.

Owns end-to-end architecture for an enterprise Generative AI platform and shared AI services. Designs scalable, secure, resilient cloud solutions involving LLMs, RAG, model serving, APIs, data flows, and agentic workflows. Establishes architecture standards, integration contracts, governance controls, and non-functional requirements while leading design reviews and proof-of-concept evaluations. Collaborates with product, engineering, data science, cloud, security, and control teams to deliver production-ready AI systems with strong observability, compliance, and operational readiness.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production AI platforms. Define technical standards across HPC, GPUs, Kubernetes, hybrid cloud, networking, storage, reliability, observability, security, and governance. Solve complex performance and scalability challenges, evaluate emerging technologies, lead cross-functional initiatives, and mentor engineers in a highly regulated environment.