Machine Learning Jobs in Cambridge, MA

Leads the design, development, deployment, and maintenance of machine learning systems at scale. Responsibilities include building ML models and data pipelines, writing production code, optimizing cloud architectures, automating testing and deployment, monitoring and retraining models, and ensuring performance, governance, security, responsible AI, and explainability. The role collaborates with Product and Data Science teams in an Agile environment focused on enterprise AI coding tools.

Own the strategy and roadmap for WHOOP’s foundational sensing algorithms. Lead cross-functional work across product, sensor intelligence, machine learning, hardware, embedded systems, and science to improve measurement accuracy, reliability, robustness, and performance. Use field data, validation, member feedback, and benchmarking to identify opportunities, define success criteria, make technical tradeoffs, and translate sensing advances into improved health and performance experiences.

Drive product development for Mastercard’s commerce media and AdTech solutions. Responsibilities include defining product concepts, requirements, roadmaps, MVPs, workflows, and technical features; partnering with Engineering, Data Science, CX&D, TPMs, and business stakeholders; supporting go-to-market strategy and business cases; conducting market research; managing global workstreams; and mentoring junior product managers.

Leads the Managed Services analytics roadmap, KPI governance, Power BI dashboard strategy, reporting automation, and AI-augmented metrics services on InterSystems IRIS. Provides technical leadership in SQL, ObjectScript, APIs, data models, interoperability, query optimization, and application performance. Establishes data-quality and reporting standards, develops predictive analytics, mentors analysts and engineers, investigates complex issues, and advises senior leadership on operational performance, risks, and investment priorities.

Supports cybersecurity, data protection, and privacy consulting projects by researching privacy regulations, analyzing privacy impact assessments, assisting with compliance training and privacy program strategies, and helping identify cyber risks and data protection challenges. The intern collaborates with advisory teams, contributes to data breach prevention efforts, and develops foundational skills in technology consulting, privacy compliance, and cybersecurity risk management.

Provides analytics-driven advisory support for mergers, acquisitions, and other business transactions. Analyzes contract data to identify risks, obligations, performance insights, and compliance indicators; develops algorithms, data models, pipelines, visualizations, and machine learning solutions. Integrates multiple data sources, validates data integrity and security, communicates findings to clients, and mentors junior team members.

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.

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 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.

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 strategy, roadmap, and execution of WHOOP’s Sleep product experience. Translate physiological data and algorithm outputs into trusted, actionable member insights, recommendations, coaching, and behavior-change experiences. Partner with engineering, machine learning, research science, and design teams; integrate Sleep capabilities across Recovery, Healthspan, and coaching; and define metrics for trust, engagement, comprehension, habit formation, and behavior change.

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.

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.

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.

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Lead technical direction for maritime autonomy capabilities across multiple programs. Develop motion planning, localization, tracking, safe maritime behaviors, COLREGS-compliant operations, and heterogeneous multi-agent collaboration for USVs, UUVs, and UAVs. Build and integrate high-performance, rigorously tested software; support deployment, vehicle testing, demonstrations, and mission-log analysis. Mentor engineers, guide architecture and roadmaps, research new algorithms, and collaborate across perception, planning, simulation, hardware, and operational test teams. Travel is typically about 25%.

Develop and deploy autonomy software for unmanned surface, underwater, and aerial vehicles in maritime missions. Responsibilities include motion planning, localization and tracking, safety assurance, COLREGS adherence, multi-agent coordination, distributed optimization, rigorous software testing, platform integration, deployment support, mission-log analysis, and autonomy R&D. The role also involves cross-functional collaboration, customer and demonstration support, roadmap development, and mentoring junior engineers. Travel is typically approximately 25% annually.

Build and deploy AI-native tools, agents, automations, and integrations supporting go-to-market teams. Develop LLM-powered workflows for CRM enrichment, account scoring, deal intelligence, and outreach. Partner with Sales, Marketing, Customer Success, Revenue Operations, Finance, Product, Engineering, and Data teams to define measurable solutions. Own projects end to end, including prototyping, backend services, deployment, monitoring, evaluation, data-quality guardrails, adoption measurement, and continuous improvement across the GTM technology ecosystem.

Build and deploy AI-native tools, agents, automations, integrations, and backend services supporting Sales, Marketing, Customer Success, and Revenue Operations. Integrate LLMs into CRM enrichment, account scoring, deal intelligence, and outreach workflows. Partner with stakeholders to define measurable solutions, improve GTM processes, monitor reliability and data quality, evaluate technology platforms, and measure adoption and business impact.