Top Machine Learning Jobs in Boston, MA

The Staff/Senior Applied Scientist will design and deploy GenAI and ML systems, focusing on multi-agent optimization, production deployment, and collaboration with engineering teams.

Develop and maintain modeling and simulation infrastructure to test multi-vehicle autonomy in operational scenarios. Design, implement, simulate, and validate autonomy software (MIL/SIL/digital twin/LVC) using C/C++ and Python on large-scale HPC/distributed simulation environments. Present technical results and support customer engagements; occasional travel and security clearance required.

Design and deploy scalable statistical and machine learning models on large health datasets (claims, EHR, pharmacy). Prototype NLP search, resolve identity/merge anomalies, collaborate with product, engineering and analytics to productionalize models, monitor performance, and drive data-driven product improvements.

Lead technical design and delivery of production AI/ML and GenAI solutions (RAG, LLMs, agents). Build, tune, deploy, and monitor models and APIs, own MLOps practices, operate cloud AI workloads, ensure software quality and responsible AI, and partner with cross-functional stakeholders while mentoring engineers.

The Lead AI Opportunity Development manages AI initiatives at BJ's, ensuring alignment with business goals, developing strategies, and collaborating with teams to evaluate and implement AI solutions.

The Principal Technical Program Manager will lead AI initiatives, oversee program execution, ensure cross-functional cooperation, and track AI project impact metrics.

The Director of CX AI will develop and execute the AI roadmap for customer experience, evaluate tools, modernize workflows, and ensure responsible AI use.

The Scientist will design novel antigens and evaluate vaccine responses using computational methods, collaborating with teams to achieve project goals in a startup environment.

Lead and deliver GenAI projects from research to production, collaborating with engineering and business partners. Design LLM-based solutions, drive multiple initiatives, mentor team members, present results to stakeholders, and realize measurable business impact.

Individual contributor applied GenAI engineer/data scientist leading ML projects end-to-end. Builds and productionizes LLM and ML solutions, partners with business stakeholders, identifies AI/ML use cases, manages multiple projects, and communicates results with clear visualizations to deliver measurable business impact.

Develop tactical insights from large structured and unstructured datasets, build and validate ML models and AI tools, design scalable resilient distributed systems, collaborate with analytics and data teams, communicate results to technical and executive stakeholders, and influence departmental strategy while independently driving technical approaches.

Lead the development of AI/ML algorithms for signals exploitation, estimation theory, and resource management. Collaborate and lead teams in a national security context.

The Lead Research Scientist develops advanced AI/ML technologies for signals exploitation and system management while leading project teams and collaborating with customers.

Design, build, and operate scalable, reliable ML inference systems, backend services, and data pipelines for health features. Translate research prototypes to production, ensure monitoring/validation within a quality-managed SaMD environment, provide technical leadership, and participate in on-call rotations to maintain uptime and reliability.

Lead the strategic roadmap for Clinical AI in Translational Research, integrating scientific needs with data science methodologies for decision-making and implementation.

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

Design, implement, and scale production-grade data and ML pipelines for risk and payments. Build ETL and feature engineering workflows, productionize models, establish observability and monitoring, participate in reviews and on-call rotations, and mentor junior engineers to ensure reliable, explainable decisioning systems.

The Partner - AI & Analytics role involves leading AI strategy in life sciences, coordinating cross-functional teams, advising on AI-driven projects, and overseeing AI investments while maintaining communication with clients and staying current with AI developments.

The Senior Solution Owner will lead product strategy and execution for AI and data solutions, overseeing product lifecycles and data migration initiatives, and ensuring effective cross-functional collaboration.

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.

Own end-to-end data science and ML features for logistics: problem framing, modeling, production deployment, monitoring, and cross-functional delivery. Focus areas include theft detection, routing intelligence, anomaly detection, and customer-facing alerting using geospatial and IoT sensor data.

As a Principal Applied Scientist, you'll define the technical vision for offer optimization and conversion modeling systems, ensuring alignment across teams while overseeing large technical initiatives and contributing to real-world machine learning solutions.

Lead development and delivery of AI-powered features for digital and wealth management. Own product roadmap, translate business needs into product requirements, partner with engineering and data science on model development and deployment, measure product performance, ensure compliance in a regulated environment, conduct customer discovery, and support go-to-market and stakeholder communication.

The Staff Machine Learning Engineer will design and build scalable ML systems for health insights, collaborating with cross-functional teams to ensure compliance and quality in production-ready services.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.