Top Machine Learning Jobs in Boston, MA

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.

Build, deploy, and maintain production machine learning models and infrastructure. Design ML components, optimize algorithms, create data pipelines, operate distributed systems, and manage cloud-based containerized services. Apply testing, CI/CD, monitoring, model governance, responsible AI, and explainability practices while collaborating with product, data science, and cross-functional engineering teams.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include developing ML algorithms, optimizing cloud-based infrastructure, constructing data pipelines, operating distributed systems, automating testing and deployment, monitoring models, and applying responsible and explainable AI practices. The role requires collaboration with product, data science, and cross-functional Agile teams.

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.

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.

Build ML applications addressing business problems, assist with data cleaning and analysis, and support software, IT operations, and technology projects. Present results to management, research machine learning and quantum computing trends, and collaborate in a team environment under senior guidance.

Develops and maintains prompts, responses, guidelines, and datasets for generative AI and large language model tools. Leads labeling initiatives with internal teams and third-party firms, trains stakeholders on LLM and dataset best practices, and supports quality assurance and testing. Requires native-level English, bilingual ability in a specified language, specialized domain knowledge, and a college degree or equivalent experience.

Leads a data science team supporting financial crime risk modeling and advanced analytics. Oversees AML/CTF model development, maintenance, monitoring, testing, and enhancement across customer risk, sanctions screening, transaction monitoring, and emerging risk. Drives LLM and machine learning innovation, develops analytical tools and dashboards, manages stakeholders, and ensures regulatory compliance, risk controls, and team development.

Lead global performance analytics across demand, inventory, product, and channel views. Translate ambiguous business questions into scalable analytical solutions, develop forecasting and advanced models, improve data quality, and productionize reusable workflows. Partner cross-functionally with Technology and Planning leaders, align metrics across regions, and communicate executive-ready recommendations. Apply Python, SQL, AI/ML, generative AI, and emerging technologies to improve decision-making, growth, profitability, inventory productivity, and operational efficiency.

Tenure-track and tenured faculty will teach undergraduate and graduate courses, mentor students and postdoctoral researchers, conduct internationally recognized and independently funded AI or computer science research, and provide university and professional service. Research may focus on physical AI, human-centered AI, AI and software engineering, or AI safety, security, and alignment. Candidates will also contribute to inclusive teaching, experiential learning, and interdisciplinary collaboration across Northeastern’s colleges and research institutes.

Individual contributor data scientist leading applied GenAI initiatives from research through production. Develops LLM-based applications, machine learning solutions, and efficient inference tools; collaborates with engineering and senior business stakeholders; identifies AI/ML use cases; manages multiple initiatives; communicates analytical findings; and mentors team members while delivering measurable business impact.

Leads enterprise strategy, portfolio governance, investment planning, and delivery roadmaps for data products, platforms, artificial intelligence, machine learning, and automation. Oversees responsible AI, platform modernization, data governance, vendor management, budgets, adoption, risk, and measurable business outcomes. Builds and leads multidisciplinary teams through Directors, Senior Managers, and Managers while partnering with business, product, engineering, security, legal, privacy, finance, and operations leaders.

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.

Tenure-track faculty will teach undergraduate and graduate electrical and computer engineering courses, lead an independent externally funded research program in embodied, wearable, assistive, or physical AI, and contribute to departmental, college, and university service. Candidates should advance interdisciplinary and translational research, mentor diverse global students, and participate in collaborative academic initiatives. Associate and full professors require established records of research, teaching, leadership, and recognized disciplinary impact.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Develop and deploy production-ready AI models, agentic applications, and RAG systems for investment and business use cases. Build data pipelines, optimize AI performance, and manage cloud-based deployments. Partner with stakeholders to gather requirements, shape AI product roadmaps, and deliver measurable value. Research emerging AI methods, establish best practices, mentor junior team members, and lead internal training.

Leads product safety and wellbeing strategy for AI-powered experiences. Owns safety roadmaps, guardrails, evaluation frameworks, benchmarks, metrics, monitoring, launch-readiness criteria, and human or automated evaluation programs. Assesses failure modes and emerging risks, including self-harm and crisis scenarios, and translates expert and user findings into product guidance. Partners with engineering, machine learning, product, design, and data science teams to improve risk detection, response generation, escalation, and user experience.

Designs and develops machine learning and artificial intelligence models, improves model performance, and applies deep learning to complex problems across research and development projects. Collaborates with technical teams and customers to define requirements, conducts research with guidance, solves well-defined problems, and communicates progress and challenges. Requires strong analytical, mathematical, software development, and communication skills.

Designs, develops, and improves AI and machine learning models for diverse R&D projects, including generative AI, predictive maintenance, data intelligence, and bioinformatics. Collaborates with technical teams and customers to define requirements, solve problems, conduct research, and deliver software solutions. Requires strong programming, analytical, mathematical, communication, and problem-solving skills, along with the ability to obtain and maintain a U.S. government security clearance.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Conduct computational oncology research using statistics, machine learning, AI, and multi-omics data. Analyze NGS, functional genomics, CRISPR, and single-cell datasets; nominate targets, biomarkers, and drug combinations; integrate biological evidence; apply foundation models and LLM-based tools; and collaborate with experimental, engineering, and program teams to support reproducible discovery research.

Develop and deploy large-scale software systems for AI education and public policy research. Build generative AI, machine learning, and NLP tools; support randomized controlled trials and empirical evaluations; collaborate with researchers; translate theoretical models into production-ready applications; and contribute to academic publications and technical reports.

Develops and evaluates machine learning and deep learning models using biomedical imaging, genomic, transcriptomic, clinical, and text datasets. Builds data pipelines and computational tools, performs feature engineering and validation, conducts cancer genomics research, and contributes reproducible code. Collaborates with computational biologists, clinicians, engineers, and external partners to identify biomarkers and generate scientific insights. Communicates findings through reports, presentations, abstracts, and manuscripts.