Machine Learning Jobs in Cambridge, MA

Lead ML engineer responsible for designing, building, deploying, and monitoring production ML systems at scale. Collaborate with product and data science teams, develop ML architectures and data pipelines, apply CI/CD and cloud-based MLOps, retrain and govern models, and ensure performance, reliability, and responsible/explainable AI practices.

Lead development and deployment of AI-driven phenotyping pipelines using EMR and real-world data to define cohorts, extract features, and validate phenotypes. Integrate clinical, genomics, and biomarker datasets, build reproducible SQL/Python/R pipelines, apply statistical genetics and rare variant analyses, and improve data infrastructure to support Genetics-led programs.

Lead the Accounting & Finance Reporting, Data & AI Center of Excellence: set strategy, governance, roadmap, and deliver analytics, automation, and AI solutions. Own intake, prioritization, implementation, adoption, and value realization across financial processes while partnering with IT and mentoring the team.

Lead client-facing, end-to-end AI strategy engagements: scope opportunities, design solutions, build roadmaps, and oversee implementation. Advise C-suite on AI adoption, value realization, governance, and technical enablement. Maintain hands-on fluency with modern AI stack and platforms, drive business development, mentor consulting teams, and contribute to an AI Center of Excellence.

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.

Seeking a Senior AI Engineer to develop AI solutions, oversee project delivery, and collaborate on system architecture. Requires 5+ years with Python and AI technologies.

Own end-to-end product design for an LLM-powered healthcare platform. Design and prototype intuitive, accessible interfaces for clinical users, collaborate with engineering, product, and clinical teams, and shape design culture and processes as an early-design hire.

As a Functional Consultant, you will drive consulting for clients in investment banking, analyze FO systems, guide business strategies, and create documentation for transformations and migrations.

Own Roku's audience targeting and activation product surface end-to-end: define audience creation, curation, distribution, and activation across buying paths; set strategy and governance; shape modeled audiences and portability to external platforms; productize new targeting signals; improve self-serve targeting workflows; define success metrics and drive cross-functional execution with engineering, data science, sales, and partners.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

The Software Engineer will develop algorithms and code related to AI, robotics, and sensing technologies, impacting key systems at Zoox.

The role involves leading product strategy and execution, managing product backlogs, and collaborating with cross-functional teams to deliver AI-driven solutions.

Serve as a technical advocate for strategic Box customers, providing white-glove support across 10-15 accounts, conducting technical and support health reviews, escalating critical issues, driving training and best practices, influencing product roadmap, engaging customers on AI use cases, and improving scalable processes for the TAM offering.

Lead multimodal oncology biomarker discovery using advanced AI/ML to integrate RNAseq, WES, ctDNA, digital pathology, proteomics, and clinical data. Drive translational analyses across internal stakeholders, validate biomarkers, inform diagnostic development, and manage a team of computational scientists supporting drug development.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

Lead multi-modal biomarker discovery in oncology by analyzing large clinico-genomic datasets (RNA-seq, WES/WGS, proteomics, imaging, single-cell, epigenetics). Apply deep learning, statistical learning, and predictive modeling, collaborate with AIML teams, and present findings to cross-functional stakeholders to inform translational and clinical development decisions.

As an AI Product Specialist at Kensho, you will drive market expansion, manage customer engagement, and leverage AI product strategies while collaborating across teams to ensure effective go-to-market execution for AI solutions.

As an MLOps Engineer at Kensho, you will develop tools and processes for ML workflows, assist ML engineers, and drive adoption of best practices across teams.

Own and set technical direction for the Everand backend, building scalable, reliable systems for discoverability, personalization, and core consumption journeys. Drive architecture, platform health (CI/CD, staging, DBs, queues, cost, observability), mentor engineers, lead cross-team planning, modernize legacy systems, and partner with product, design, analytics, web/mobile, and ML to support experimentation and telemetry-driven decisions.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

This role focuses on conducting research in equity markets using ML and NLP for alpha generation. You'll collaborate with global teams to develop models and translate innovations into strategies.

Implement and maintain record-matching and text-analytics components, translate linguistic theory into production features, optimize search internals (Elasticsearch), collaborate in agile sprints, and help design scalable APIs and architectures to improve record-matching quality and reliability.

Develop computational methods to process and interpret nanopore and electrophysiology data. Build event-detection, classification, and fingerprinting pipelines; create quantitative models linking signals to molecular identity; collaborate with experimental teams and ML, protein, and signal-processing groups; establish benchmarking and uncertainty quantification practices.

Lead and build a computational sciences team to develop algorithms, ML models, and scalable data infrastructure for nanopore-based protein reading. Partner with experimental teams to design analyses, implement ML pipelines, manage cloud infrastructure, establish reproducible data practices, protect IP, and communicate progress to leadership and external partners.

Design, prototype, and implement quantitative models and data-driven trading analytics using statistics, machine learning/AI, and time series forecasting. Develop code, product specs, and processes; collaborate with Global Markets stakeholders and tech teams; rigorously test model efficacy and deliver polished presentations to drive adoption.