Top Machine Learning Jobs in Boston, MA

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 and manages an AI Quality engineering team responsible for evaluating and improving AI outputs. Defines testing frameworks for model performance and drift, establishes technical strategy and architecture, provides technical direction, manages technology vendors and partnerships, drives engineering quality improvements, and mentors team members.

Lead the technical direction and execution of WHOOP’s foundation AI modeling efforts across wearable sensor data, language, biomarkers, and behavioral signals. Drive high-risk research from hypothesis to production, shape AI strategy and infrastructure decisions, collaborate across research, product, and engineering, mentor senior technical talent, establish evaluation standards, and represent WHOOP externally through recruiting, partnerships, and the machine learning research community.

Leads the technical strategy, roadmap, and execution for WHOOP’s core physiological sensor algorithms and on-device intelligence. Manages machine learning and signal processing teams, guides architecture tradeoffs involving accuracy, latency, power, and system constraints, and delivers sensor-driven features from research through production. Partners across firmware, hardware, product, and data science while developing technical leaders and establishing scalable algorithm development and validation practices.

Design, train, test, optimize, and deploy machine-learning and deep-learning models using time-series and biosensor data. Develop robust inference systems for constrained edge and cloud environments, monitor algorithm performance and data quality, support regulated health-feature documentation, and conduct applied research. Collaborate with data science, firmware, and engineering teams to translate research prototypes into scalable production features for wearable technology.

Design, build, and evaluate generative AI and agentic systems; develop enterprise semantic layers and knowledge graphs; create GenAI evaluation frameworks assessing performance, reliability, and safety; collaborate across data science, engineering, and business teams; champion best practices; lead cross-functional working groups; communicate technical findings; and provide technical consulting on high-impact initiatives.

Senior technical leader defining 10-year scientific visions and systems architecture for Bose Research franchises. Ensures coherence across AI/ML, sensing, signal processing, and edge domains; guides foundational IP, models, datasets, and evaluation frameworks. Oversees program architecture, technical governance, and translation of research into scalable platforms and products while representing Bose externally and collaborating with research and engineering stakeholders.

Manage delivery of a portfolio of AI and data science projects from planning through implementation. Coordinate technical and non-technical teams, dependencies, requirements, budgets, timelines, and stakeholder expectations. Lead Agile ceremonies, drive accountability, translate AI capabilities into actionable requirements, and proactively identify and mitigate risks across complex, concurrent initiatives.

Architect complex embedded hardware and software systems for next-generation audio products. Evaluate feasibility, tradeoffs, scalability, and system constraints across power, compute, memory, latency, performance, and security. Develop simulations, prototypes, AI agents, and automated engineering workflows for architecture exploration and verification. Translate audio, sensing, machine learning, and AI technologies into reusable platforms, frameworks, algorithms, and IP while providing principal-level technical leadership across engineering teams, suppliers, and partners.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

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 and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Lead design and implementation of AI and blockchain-enabled integration and platform architectures. Analyze requirements, develop technical designs, direct large projects, validate outcomes with clients, coach diverse teams, drive cloud strategy (Azure), perform feasibility studies and business process modeling to optimize enterprise technology infrastructure.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Leads product vision, roadmaps, prioritization, and delivery for enterprise data science enablement capabilities. Serves as Product Owner, translating customer and stakeholder needs into backlogs and technical solutions. Partners with data scientists, engineers, architects, and business teams to support AI/ML development, deployment, monitoring, adoption, and business value realization. Drives reusable platform capabilities, technical debt reduction, DevOps and MLOps improvements, Agile delivery, and executive communication.

Lead technical vision and execution for AI/ML, decision systems, and mission planning programs. Manage multidisciplinary teams, ensure schedule and budget adherence, provide technical oversight, cultivate external partnerships, and pursue business growth while guiding program delivery and technical strategy.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

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.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Lead data science projects to advance drug development, implementing models for patient selection and clinical trial optimization, and collaborating with cross-functional teams.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

Leads client-facing data science and analytics initiatives, translating complex marketing and media data into actionable business narratives. Develops measurement strategies, including test/holdout incrementality, informs acquisition and retention decisions, scales analytics best practices, and manages analyst teams. Partners with Sales, Client Services, Operations, and other stakeholders while supporting predictive and adaptive AI/ML solutions. The role requires strong client communication, strategic leadership, analytics expertise, and proficiency with Python, SQL, Tableau, and related tools.