Top Senior Machine Learning Jobs in Boston, MA

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 end-to-end machine learning projects to improve customer engagement and retention. Build, test, and deploy models for user forecasting, personalization, and Casino engagement. Partner cross-functionally to scale solutions, mentor junior data scientists, and communicate model impact to influence product and marketing strategy.

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.

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 the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

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

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.

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.

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

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

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

Lead design, build, deploy, and maintain production ML and AI systems (including LLM inference, RAG, similarity search, observability, and governance). Collaborate cross-functionally to create data pipelines, retrain and monitor models, and apply responsible, explainable AI practices while contributing technical vision and roadmap for AI solutions.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Lead and mentor ML engineers and data scientists, overseeing ML strategy and development of scalable models for healthcare applications.

Develop and maintain geometry processing, simulation, and rendering algorithms; build services to read/process 2D/3D engineering data; curate large datasets; implement ML post-training workflows; and contribute to domain-specific languages for engineering.

Lead actuarial data scientist to oversee technical pricing models, actuarial systems, and a team of actuarial data scientists. Develop strategic roadmap, establish modeling standards and validation, apply ML/AI to pricing, collaborate with legal/compliance, present insights to executives, and manage projects to deliver high-quality actuarial products.

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.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Lead development of motion planning and prediction ML models for Level 4 autonomous vehicles. Design and productionize behavior models using deep learning, generative AI, and reinforcement learning. Deploy solutions to vehicle fleets, collaborate with perception, simulation, data platform and integration teams, and report progress to leadership.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

Lead development and deployment of cutting-edge ML models for pricing, design and analyze experiments, collaborate with product and engineering, present findings to stakeholders, and lead technical projects to influence team output and business metrics.