Machine Learning Jobs in Cambridge, MA

Develop scalable computational pipelines for biophysical modeling of neurons and neural populations. Apply machine learning and statistical inference to large-scale neural recordings, construct bio-realistic networks, and analyze complex time-series data. The role also involves shaping the technical roadmap, prototyping novel modeling approaches, and collaborating across neuroscience, experimental biology, and AI teams in an ambiguous R&D environment.

Provides technical leadership throughout the enterprise customer lifecycle, including discovery, solution design, demonstrations, proof-of-concept activities, implementation, integration, adoption, and expansion. Partners with Sales, Product, Engineering, Clinical, Support, and Operations; manages integration partners; translates customer requirements into scalable workflows; supports healthcare security and compliance approvals; develops dashboards, playbooks, documentation, and training; and communicates customer insights to product teams.

Design, develop, and deploy machine learning models for indoor positioning and perception. Optimize model architectures, create real-world benchmarking tools and datasets, translate research into production pipelines, and collaborate with engineering and product teams to ship enterprise features. The role requires advanced expertise in time-series machine learning, transformer and probabilistic models, production coding, and research publication, with opportunities to apply computer vision, wireless localization, sensor fusion, and mobile ML deployment.

Designs, builds, and operationalizes machine learning models and production AI systems, including classical ML, deep learning, and LLM solutions. Develops end-to-end ML pipelines with CI/CD, monitoring, and performance tracking; establishes scalable and secure architecture standards; applies MLOps practices; collaborates with technical and business teams; and mentors junior engineers. The role focuses on NLP, document understanding, unstructured data, distributed computing, cloud platforms, and production-grade engineering.

Own machine learning problems end to end, including data, modeling, evaluation, deployment, monitoring, and production optimization. Build training, inference, evaluation, datasets, and benchmarking pipelines for indoor positioning and item localization. Collaborate with applied scientists, engineering, and product teams to deliver enterprise features while optimizing accuracy, coverage, latency, and cost. Contribute reusable infrastructure, architecture decisions, and high engineering standards.

Own the vision, roadmap, backlog, and outcomes for data products delivered by data science, AI engineering, and intelligent automation teams. Translate business problems into AI and automation initiatives, prioritize investments, evaluate emerging technologies, and lead pilots through production. Partner with stakeholders and agile delivery teams, validate data and model performance using SQL, ensure responsible governance and scalability, and communicate outcomes to technical and executive audiences.

Develop statistical and machine learning algorithms for weather, climate, and catastrophe modeling. Collaborate with scientists and engineers, analyze large atmospheric datasets, and conduct model sensitivity and validation testing. The role requires advanced knowledge of statistical inference, extreme value models, distribution fitting, estimation methods, and goodness-of-fit testing, along with demonstrated machine learning experience.

Leads advanced people analytics using Workday as the foundation for workforce insights, planning, retention, compensation, and talent decisions. Develops analytical datasets, metrics, dashboards, predictive models, and executive reporting; establishes data definitions, governance, quality controls, and architecture standards. Partners with HR Technology, Finance, HR leaders, and business executives to translate complex workforce data into strategic recommendations. Serves as a Workday and people-data subject-matter expert, mentors analysts, and influences enterprise decisions through clear storytelling.

This 12-week engineering internship supports P&G technical centers and manufacturing sites. Interns contribute to automation and controls, mechanical design, packaging, manufacturing process development, instrumentation, robotics, and environmental or civil engineering projects. Responsibilities include technical analysis, prototyping, process optimization, equipment and control-system development, validation, supplier collaboration, and sustainability initiatives. Interns receive mentorship, training, hands-on project ownership, and exposure to advanced manufacturing and Industry 4.0 technologies.

Lead SRE ownership of ML platform SLOs and operational health for Ray on EKS and Databricks on EC2. Maintain observability with CloudWatch/Datadog, tune autoscaling and GPU scheduling, manage Databricks workspaces and IAM, optimize cost/capacity, codify infrastructure with Terraform and CI/CD, lead incident response and postmortems, perform security/OS maintenance, and participate in on-call rotation.

Conduct research in sensor fusion and spatial AI, develop indoor positioning and spatial perception algorithms, run experiments on customer deployments, analyze failures, and improve production machine learning and signal processing pipelines. The role collaborates with engineering and product teams to ship enterprise features and requires a PhD in computer science, electrical engineering, robotics, or a related field.

Conduct research and develop production algorithms for indoor positioning, spatial perception, sensor fusion, and spatial AI. The intern will run experiments on real-world customer deployments, analyze failures, improve ML and signal-processing pipelines, and collaborate with engineering and product teams to ship features to enterprise customers.

Analyze crop protection and sensor data, develop statistical and machine learning models, build dashboards and recommendation engines, improve perception pipelines, establish data quality measures, and communicate findings across technical and agricultural teams.

Designs, builds, and operates production machine learning systems at scale. Develops deep learning models for sequential, multimodal, and spatio-temporal data supporting risk assessment, engagement, and event detection. Partners with research, product, and platform teams to deploy reliable ML products across cloud and edge environments. Responsibilities include software engineering, AWS-based deployment, complex SQL, large-scale data processing, model orchestration, and cross-functional delivery of measurable mobility and road-safety impact.

Own enterprise sales and client relationships for Aera’s decision intelligence platform. Responsibilities include targeting and closing large accounts, expanding existing clients, engaging C- and VP-level stakeholders, developing transformation roadmaps and business cases, articulating platform value, managing pipelines, collaborating cross-functionally, and meeting revenue targets. The role requires consultative enterprise software sales experience and frequent travel, with preference for candidates familiar with data analytics, AI/ML, RPA, process mining, and supply chain operations.

Drive new-account acquisition and cloud migrations for AI and infrastructure solutions. Generate pipeline through global outbound prospecting, execute multichannel outreach, manage the full enterprise sales cycle, and close large cloud, infrastructure, and data center deals. The role requires deep knowledge of AI computing, cloud infrastructure, power capacity, cooling systems, financial modeling, and B2B enterprise net-new cloud sales. Candidates work from Akamai’s San Francisco office and collaborate across time zones.

The Manager of Data Science will lead a team focused on machine learning model development, team management, and cross-functional collaboration to advance product goals.

Lead and manage consulting engagements delivering power market analysis, price forecasts, asset valuations, transaction support, and strategic advisory. Oversee modeling (PROMOD, PSLF, PLEXOS, Aurora), mentor staff, manage client relationships, drive business development, publish thought leadership, and contribute to proprietary forecasting and analytical tools across U.S. power markets and technologies including renewables, storage, transmission, and large loads.

As a Research Scientist, you will develop innovative machine learning techniques for clinical applications, design large language models, and collaborate with engineering teams while advancing NLP research.

Drive business development in biotech and biopharma, managing strategic accounts and closing enterprise deals. Partner with cross-functional teams for project execution and provide customer insights to shape product direction.

Serve as an AI-fluent implementation consultant for Veeva Vault Safety: advise on AI strategy, design validation and governance frameworks, build monitoring dashboards, run workshops, lead gap analyses and configurations, train customers, and ensure GxP/CSV compliance throughout implementations.

Lead the design, training, evaluation, and deployment of large-scale machine learning models, including LLMs, diffusion models, and GNNs. Own distributed training pipelines, dataset interfaces, optimization strategies, model evaluation, serving, and lifecycle management. Collaborate with data, infrastructure, and scientific teams while providing technical leadership through architecture reviews, mentorship, and cross-functional decision-making.

Summer Engineering Intern contributing to production frontend and backend features, APIs, services, and machine learning systems. The role involves writing tested Python and Supabase code, collaborating with engineering, design, and product teams, using cloud and DevOps tools, and applying AI coding agents. Candidates will learn through pairing, code reviews, and direct feedback while building scalable infrastructure technology for government customers.

Engineering intern contributing to production frontend and backend features, APIs, services, and machine learning capabilities. Responsibilities include writing tested Python and Supabase code, collaborating with engineering, design, and product teams, using cloud and DevOps tools, and working with AI coding agents. The intern will receive mentoring through pairing, code reviews, and feedback while building scalable software for infrastructure customers.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.