Machine Learning Jobs in Cambridge, MA

Data Science Interns will support the development, deployment, and operation of machine learning solutions in production systems. They will contribute to model development, engineering design, scalable deployment, internal R&D, and projects involving recommendation engines, fraud detection, and marketing optimization. Interns will collaborate with experienced data science and machine learning engineers and receive mentorship while learning end-to-end workflows.

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.

Optimize GPU performance for Zoox’s motion planner, including profiling GPU hotspots, adapting software to multiple GPU architectures, reducing ML model latency and memory usage, and developing or optimizing CUDA kernels. Serve as a CUDA and GPU performance subject matter expert while working with complex multiprocess robotic systems in a Linux and C++ environment.

Lead product strategy and execution for Iris Chat, an AI-powered psychological fitness product. Own product vision, customer journeys, activation, retention, conversion, subscription value, experimentation, and AI quality. Manage product, design, engineering, and AI research teams; hire and develop leaders; balance research and delivery; and establish standards for evaluation, privacy, safety, reliability, and user trust. Partner with leadership to build a focused roadmap and durable consumer business.

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.

Lead cybersecurity, AI, and technology risk assessments across systems, cloud environments, business processes, and initiatives. Maintain the enterprise cyber risk register, track mitigation plans, translate technical findings into business risks, support FAIR-based quantitative analysis, and prepare executive reporting. Partner with Security, Legal, Product, IT, and business teams to assess architectures, AI use cases, emerging technologies, and third-party integrations while strengthening GRC processes and operational resilience.

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.

Conduct end-to-end qualitative and quantitative user research across WHOOP’s hardware, software, machine learning, and emerging technology products. Plan studies, engage participants, moderate interviews and usability sessions, analyze findings, and synthesize actionable recommendations. Collaborate with Product, Design, Hardware, and Machine Learning Research teams to inform product decisions. Lead sequential research projects, evaluate emerging and AI-enabled research methods, and help strengthen research practices while maintaining methodological rigor.

Leads cross-functional execution for WHOOP’s Core Algorithm Development team, coordinating engineers, data scientists, firmware, software, product, and validation teams. Owns program planning, milestones, dependencies, risk mitigation, algorithm validation, integration, release readiness, reporting, and executive communications. The role establishes scalable development and release processes to improve delivery predictability, quality, and velocity across concurrent physiological algorithm programs.

Leads WHOOP’s Distributed Research team and strategy for large-scale decentralized data collection. Responsibilities include managing and developing researchers, prioritizing initiatives, allocating resources, partnering with research, product, engineering, and platform teams, improving research technologies and workflows, monitoring performance metrics, and advocating for distributed research and survey science best practices.

Develop sensor intelligence for physiological insights by owning sensor modality requirements, processing raw data, implementing signal-processing and machine-learning algorithms, designing hardware and human studies, and collaborating with hardware and firmware teams. The role requires communicating technical findings across functions while advancing sensor and product features from early concept through development.

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.

Own the AI product platform, including evaluation methodology, model and provider selection, internal tooling, adoption, and AI unit economics. Partner with engineers, researchers, and data scientists to improve quality, latency, and cost. Drive cross-functional programs, assess emerging ML techniques, and turn ambiguous opportunities into focused bets for internal productivity and member experiences.

Leads machine learning engineering and applied science teams developing regulated health features and SaMD products. Owns delivery, hiring, people development, operating models, validation rigor, quality gates, risk management, audit readiness, and regulatory submission timelines. Partners with regulatory, quality, clinical, product, software, and firmware stakeholders while overseeing ML lifecycle decisions, traceability, governance, and AI-enabled workflows.

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.

Designs and scales high-fidelity security detections across cloud, identity, endpoint, network, and application environments. Develops Detection-as-Code rules aligned with MITRE ATT&CK, translates threat intelligence into detections, and validates coverage through adversary emulation. Tunes alerts, reduces false positives, automates enrichment and triage, tracks detection KPIs, supports incident investigations, participates in on-call rotations, and partners with engineering, infrastructure, product, IT, and GRC teams. Applies analytics and machine learning to improve detection fidelity and investigation workflows.

Leads global teams and client engagements delivering Life Sciences commercial analytics, sales effectiveness, field force design, incentive compensation, targeting, call planning, and alignment solutions. Partners with stakeholders to translate business problems into technology-enabled solutions, presents executive recommendations, manages concurrent projects, develops talent, supports hiring, and creates thought leadership content. Requires 8–12 years of Life Sciences commercial analytics experience and up to 25% travel.

Owns end-to-end architecture for a major generative AI platform and shared AI services. Responsibilities include designing model integrations, APIs, data flows, orchestration, RAG and agentic workflows; defining architecture standards, integration contracts, and non-functional requirements; ensuring security, resilience, compliance, governance, observability, and scalability; and conducting architecture reviews and proof-of-concept assessments. The role partners with product, engineering, data science, cloud, security, and control teams to deliver production-ready enterprise AI systems.

Lead biostatistical support for Phase I–IV clinical trials, including study design, sample size calculations, exploratory and efficacy analyses, simulation studies, statistical analysis plans, regulatory submissions, and interpretation of results. Provide strategic guidance to cross-functional teams, oversee vendor-supported work, apply advanced statistical methods, and communicate technical findings clearly to technical and non-technical stakeholders.

Leads global patient strategy and market development for Dysport in migraine, using patient insights, segmentation, analytics, AI, and journey mapping to improve disease awareness, diagnosis, referral, preventive-treatment consideration, and engagement. Partners with medical, market access, analytics, digital, regional, and local teams; manages agencies, budgets, launches, advocacy, and performance measurement across global markets.

Works directly with founders to identify, evaluate, launch, and scale new ventures across industries, geographies, partnerships, and customer offerings. Responsibilities include developing business cases, setting milestones, recommending investments, recruiting initial teams, coordinating engineering and commercial leaders, supporting fundraising through financial analysis, and deciding whether to scale, revise, stop, or transition initiatives.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Lead cross-functional technical programs from scoping through rollout, including AI model launches and multi-team engineering initiatives. Build Suno’s TPM function, establish operating principles, coach TPMs, and partner with engineering leadership to improve team velocity and deliver coordinated launches. The role requires strong program management experience, technical and coding fluency, adaptability, and the ability to measure program success in an ambiguous, fast-growing consumer technology environment.