Top Senior Machine Learning Jobs in Boston, MA

Lead and direct multidisciplinary teams to research, develop, test, integrate, and demonstrate advanced signal processing, optimization, and machine learning algorithms for radar, electronic warfare, and sensor systems. Oversee modeling/simulation, algorithm T&E, customer engagement, and technical briefings for defense and intelligence applications.

Perform data analysis and test & evaluation of machine learning algorithms; develop algorithms for physical and autonomous systems; transition algorithms to software and embedded systems; work with stakeholders to present results and solutions; research new ML and data-analysis methods; handle large, multidimensional datasets and data pipelines.

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

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Lead ML engineer responsible for architecting, building, deploying, and operating production-scale machine learning systems. Collaborate with product and data science teams to design models, data pipelines, CI/CD, and cloud architectures; retrain and monitor models; ensure performance, reliability, governance, and explainability; and mentor or lead engineering teams.

Lead a small team to design, build, deploy, and monitor ML/AI solutions for Claims (fraud, triage, severity, litigation, subrogation). Manage projects, collaborate with engineering and IT, follow model governance, and mentor staff.

Lead training, evaluation, and deployment of ML models for scene understanding and behavior prediction in autonomous vehicles.

You will train, evaluate, and deploy ML models for scene understanding and behavior prediction in autonomous vehicles, while collaborating with engineers.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

Lead end-to-end development, validation, and deployment of large-scale predictive models. Drive rigorous data-driven research, translate business goals into analytical frameworks, and communicate findings to executives. Mentor data scientists and partner with engineering and stakeholders to productionize ML solutions and define long-term analytics strategy.

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.

Embed with Marketplace teams to discover and prioritize AI opportunities, rapidly prototype and validate AI/LLM solutions using enterprise data, and partner with Technology and Data teams to productionize, measure, and drive adoption of scalable automation and ML solutions.

Apply advanced AI, ML, and computational modeling to drug discovery and early development. Build scalable data pipelines and predictive models, integrate diverse scientific datasets, ensure model validation and reproducibility, and communicate insights to multidisciplinary teams to support decision-making.

Lead global strategy to optimize and modernize financial crime detection across transaction monitoring, customer risk rating, and fraud models. Drive transition from rules-based controls to AI/ML-driven detection, oversee model lifecycle governance, reduce false positives, ensure regulatory defensibility, and partner with Compliance, Technology, Data, Operations, and vendors to deliver measurable risk and operational improvements.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

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

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.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

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