Top Senior Machine Learning Jobs in Boston, MA

Develop and deploy signal-processing algorithms and machine-learning models that convert wearable sensor data into real-time physiological insights. Responsibilities include data analysis, model training, Python prototyping, optimized C/C++ implementation, embedded firmware integration, and production validation. The role requires collaboration with firmware, hardware, data science, and domain teams to optimize accuracy, power, memory, compute, and latency while conducting rigorous offline, laboratory, real-world, and on-device testing.

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.

Develops, tests, evaluates, and deploys machine learning models using established architectures and frameworks. Supports feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborates with engineering and product teams on AI requirements, researches methods to improve model performance, and supports urgent IT incidents. The role requires strong Python and machine learning experience, knowledge of deep learning and generative AI, and advanced quantitative education.

Develop, test, deploy, and serve machine learning models and agentic AI capabilities. Build feature engineering, experimentation, training, monitoring, and REST-based model-serving workflows. Collaborate with data science, engineering, product, and MLOps teams to productionize AI solutions at enterprise scale using distributed computing, Databricks, cloud platforms, and containerized infrastructure. Support model performance improvements, generative AI initiatives, and urgent IT escalations.

Build and scale core ML platform infrastructure for model training, deployment, feature storage, real-time retrieval, inference, and monitoring. Lead architecture and technical execution, write production backend code, conduct design and code reviews, mentor engineers, improve reliability and cost efficiency, and partner with ML, product, data science, infrastructure, and security teams on platform strategy and roadmap.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Leads the technical direction, architecture, and roadmap for agentic machine learning systems processing global trade data. Builds ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback systems. Adapts and post-trains LLMs, ships production AI agents, mentors engineers, sets technical standards, and partners with product, engineering, data science, and customers to deliver risk, compliance, and enforcement capabilities.

Lead the technical direction and development of production machine learning and agentic systems for global trade risk, compliance, and enforcement. Responsibilities include designing data-to-decision pipelines, adapting and post-training LLMs, building evaluation and observability systems, improving model performance, mentoring engineers, and partnering with product, customers, and cross-functional teams to ship AI capabilities.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

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.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Lead architecture and development of advanced perception systems for Level 4 autonomous vehicles. Build multimodal foundation models, synthetic data and sensor simulation capabilities, knowledge-distillation pipelines, and efficient real-time onboard models. Deploy solutions to autonomous vehicle fleets while mentoring engineers and providing technical leadership. The role focuses on object detection, segmentation, tracking, classification, sensor fusion, closed-loop evaluation, and robust generalization across vehicle generations.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

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.

Build and integrate Atlas humanoid robot agent systems across harness design, context engineering, tool use, memory, evaluation, observability, dashboards, and diagnostics. Develop reliable planning and reasoning capabilities using VLMs and agentic systems, benchmark performance and safety, collaborate with robotics and product teams, and ship full-stack applications for real-world use cases.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.