Top Machine Learning Jobs in Boston, MA

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Leads data science initiatives for customer financial resiliency, partnering with engineers and product managers. Builds, validates, implements, and productionizes supervised and reinforcement learning models across large-scale data. Uses Python, cloud platforms, open-source machine learning tools, and statistical methods to deliver personalized customer solutions. Translates complex analyses into business outcomes, evaluates emerging technologies, and develops team talent.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Leads enterprise data science initiatives for agentic AI products, including AI/ML model development, experimentation, telemetry analysis, and strategic recommendations for senior leadership. Partners with data scientists, engineers, and product managers while translating complex statistical insights into business outcomes. Requires extensive experience in quantitative analytics, large-scale data analysis, production AI/ML deployment, A/B testing, and team leadership.

Leads hands-on development and evaluation of generative AI, agentic systems, MLOps solutions, semantic data layers, knowledge graphs, and GenAI evaluation frameworks. Collaborates across data science, engineering, and business teams; champions best practices, leads working groups, provides technical consulting, and communicates complex findings to stakeholders. This is an individual contributor role focused on enterprise data strategy and AI/ML innovation.

Design, develop, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Establish enterprise AI architecture standards, optimize model performance and infrastructure costs, lead hybrid and multi-model orchestration, promote responsible AI, and mentor engineering teams while influencing technical strategy and research-to-production processes.

Lead data science for US Card customer management by partnering with data scientists, engineers, product managers, and business teams. Build, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical modeling, clustering, classification, sentiment analysis, time series, and deep learning to generate customer insights and forecasts. Translate technical findings into business outcomes while contributing to innovation, stakeholder collaboration, and talent development.

Build, deploy, scale, and monitor production machine learning models and infrastructure for risk management. Responsibilities include model development, data pipeline construction, cloud and distributed systems operation, Kubernetes-based deployment, CI/CD, model governance, responsible AI, testing, monitoring, and collaboration with product and data science teams.

Develop and productize advanced audio DSP and machine learning algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Translate research into scalable, optimized, real-time solutions for consumer devices and production hardware. Balance audio quality, ML performance, and system constraints while influencing product direction. Collaborate across algorithm research, systems engineering, deployment, and productization.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Build and deploy machine learning models and scoring platforms for credit decisioning and valuation. Analyze large-scale financial and textual datasets using statistical modeling, cloud technologies, and open-source tools. Responsibilities include model design, training, validation, implementation, analytical tool development, alternative data analysis, and collaboration with software engineering and product teams to deliver scalable credit products.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

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.

Manage delivery of a portfolio of AI and data science projects from planning through implementation. Coordinate technical and non-technical teams, dependencies, requirements, budgets, timelines, and stakeholder expectations. Lead Agile ceremonies, drive accountability, translate AI capabilities into actionable requirements, and proactively identify and mitigate risks across complex, concurrent initiatives.

Leads data scientists and machine learning engineers developing personalized customer experiences at scale. Sets team strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; guides machine learning systems through development, deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability; and manages capacity, resources, technical standards, and team development.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

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.

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.

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.

Develop and productize machine learning and digital signal processing algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Research modern deep learning approaches, prototype with Python or MATLAB, and deploy optimized real-time solutions on consumer hardware. Collaborate across research, systems engineering, and product development to deliver robust audio technologies at scale.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

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.