Top Machine Learning Jobs in Boston, MA

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining modeling strategy, designing experiments, analyzing telemetry, influencing senior leaders, validating and stress-testing models, and overseeing the full model lifecycle from design through implementation. The role partners with engineering and research teams and requires extensive experience in large-scale analytics, machine learning, relational databases, cloud computing, and open-source programming.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Build and deploy AI-enabled solutions for Marketplace operations and commercial challenges. Identify opportunities, frame ambiguous business problems, develop prototypes using enterprise data and AI/LLM tools, validate business value, and transition successful solutions into production. Partner with business, product, technology, and data teams; drive adoption, measure outcomes, coordinate initiatives, mentor team members, and enhance reusable platforms and frameworks.

Conduct research and develop AI planning and machine-learning algorithms for multi-robot systems. Identify optimization opportunities, adapt or create high-performance approaches, run simulations and experiments, and validate system performance. Present findings internally and at academic venues while collaborating with robotics and software engineering teams.

Leads enterprise AI and data engineering initiatives, designing solutions for document processing, intelligent agents, automation, and large-scale data platforms. Defines data architecture for fund accounting, custody, and reference data while establishing reusable frameworks, governance, lineage, quality controls, and scalable pipelines. Partners with business and technology stakeholders, guides architecture decisions, manages delivery priorities, promotes responsible AI practices, and mentors teams in a regulated financial-services environment.

Owns the vision, roadmap, governance, and lifecycle management of AI/ML platform capabilities. Translates stakeholder needs into platform requirements, prioritizes integrations and improvements, partners with engineering teams, drives adoption, and monitors reliability, performance, security, cost, and technical debt. The role also supports agile delivery, communicates releases and incidents, reports progress to leadership, and assists with critical IT escalations.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Develop production-ready machine learning and signal-processing algorithms for wearable physiological sensor data. Responsibilities include designing and evaluating deep learning models, analyzing large datasets, optimizing algorithms for embedded edge deployment, and balancing accuracy, power, memory, latency, and scalability. The role owns projects from research and prototyping through validation, deployment, monitoring, and continuous improvement while collaborating with scientists, engineers, hardware experts, and product teams.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.

Collaborate with scientific teams to identify high-impact AI opportunities, define future-backward implementation plans, assess data needs, and build and evaluate machine learning solutions. The intern will use scalable GPU resources, develop benchmarks, iterate across model approaches and scientific domains, and produce comprehensive analyses supporting new company creation.

Leads an Applied AI team delivering production machine learning and advanced analytics solutions across business workflows. Owns portfolio prioritization, technical architecture, responsible AI evaluation, operational readiness, value realization, reusable solution patterns, and external partnerships. Provides hands-on support through prototyping, exploratory analysis, code review, and design guidance while developing data science talent and influencing business, technology, governance, risk, security, and executive stakeholders.

Leads end-to-end advanced analytics projects supporting regulatory operations and cross-market surveillance. Defines quantitative approaches, develops predictive pipelines, drives data and infrastructure availability, and delivers analytics capabilities across teams. Engages stakeholders, gathers use cases, provides research and insights, mentors analysts, and advances enterprise analytics strategy. Requires strong programming, machine learning, data mining, and analytical skills in a hybrid environment with occasional travel.

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.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Research and prototype AI-powered tools for music generation, transformation, remixing, and audio understanding. Build and evaluate machine learning models using Python and modern frameworks, process audio datasets, assess model performance and musical quality, and collaborate with engineering, music technology, product, and creative teams. The intern will also document findings and contribute to responsible AI practices involving artist consent, music rights, and ethical data use.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Build and operate a scalable LLM inference platform integrated with the MAX stack. Develop distributed, multi-node model deployments, high-throughput batching, KV-cache management, autoscaling, observability, cold-start optimizations, and multi-cloud capabilities. Create Helm charts and Kubernetes operators while collaborating with kernel and generative AI teams to optimize performance. The role requires backend engineering, Kubernetes operations, machine learning experience, and distributed systems expertise.

Design, develop, and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Integrate telephony systems, LLM applications, and real-time communication platforms while improving observability, reliability, deployment, and SRE practices. Build tested, maintainable software across telephony and AI layers, support production environments, collaborate with engineering and product teams, and mentor colleagues.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.

Develops data science, machine learning, and generative AI solutions for customer engagements and pre-sales opportunities. Translates ambiguous business challenges into analytical use cases, prototypes, hypotheses, demonstrations, and actionable recommendations. Explores customer data, applies statistical and advanced analytical techniques, communicates findings to technical and business audiences, and creates reusable analytics assets. Partners across Microsoft teams to connect customer data and AI opportunities with broader transformation priorities while applying responsible AI, security, governance, and data quality principles.

Conduct machine learning and data science research for drug discovery, focusing on mass spectrometry, computational biology, and computational chemistry. Own projects from scientific question through results, develop data-driven solutions, validate AI-generated analyses, and communicate findings through visualizations and writing. Collaborate with the Data Science team and contribute to applications of AI in medicine development.

Build and deploy production machine-learning models for member identification, personalization, provider search, and healthcare AI tools. Lead projects from problem framing and prototyping through product handoff, design experimentation and causal analysis, define product metrics, and develop reliable feature pipelines and experiment infrastructure. Partner with product, engineering, care operations, marketing, and analytics engineering while communicating recommendations clearly and ensuring models are monitored, tested, and impactful.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, and end-to-end ML pipelines. Define AI/ML use cases, transition roadmaps, cognitive adoption metrics, and data-mining strategies. Participate in SAFe Agile delivery, write user stories, remove technical impediments, support DevSecOps integration, and produce transition and knowledge-transfer materials.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.