Machine Learning Jobs in Cambridge, MA

The Senior Machine Learning Engineer will develop and maintain algorithms/services for multi-agent AI, deliver cloud-native architecture, and optimize ML models for performance.

Lead client-facing finance transformation engagements to optimize finance functions through operating model design, process improvement, automation and AI. Manage project teams and client relationships, deliver high-quality recommendations and tools, identify value-creation opportunities across G&A, support business development, and coach junior staff.

Architect and engineer core software for robotic and machine learning systems, contribute to project planning, and collaborate within teams.

Generate:Biomedicines invites interested candidates to submit resumes for potential future openings in machine learning and biological engineering.

The role involves developing software for robots to enhance capabilities in vision, motion, and control, focusing on complex problem-solving.

The Head of Data Science & AI Engineering leads Ipsen's technical strategy in advanced analytics and AI, overseeing data products, mentoring teams, and ensuring compliance in a dynamic biopharmaceutical environment.

Lead Vertex's enterprise AI governance program: define strategy, Responsible AI framework, policies, risk management, and the AI governance platform. Partner with Legal, Privacy, Security, Quality, and business teams to ensure compliant, auditable, and scalable AI adoption across the company.

Lead exchange optimization for Roku’s programmatic CTV advertising platform. Own strategies for dynamic price floors, traffic shaping, auction mechanics, deal optimization, and supply-demand forecasting. Partner with engineering, data science, and demand teams to productionize optimization models, run experiments, monitor exchange health, and improve revenue, fill rate, latency, win rate, and user experience. Define technical product requirements, guide roadmap execution, and communicate strategy to executives and industry partners.

This role involves owning customer engagements, building demos, performing technical discovery, and creating reusable assets in AI solutions.

Patent Counsel II manages patent prosecution and intellectual property strategy for molecular diagnostics, sequencing, bioinformatics, AI/ML, software, and computational biology technologies. The role drafts and prosecutes domestic and international patent applications, conducts patentability and freedom-to-operate analyses, coordinates outside counsel, advises on trade secrets and publications, supports licensing and litigation matters, and counsels scientific, technical, and business stakeholders on IP risks and opportunities.

Design and scale machine learning infrastructure across AWS and on-premises environments. Lead MLOps architecture, automation, CI/CD, deployment, monitoring, and reproducible ML workflows using Kubernetes, Airflow, and related tools. Collaborate with engineering and data science teams, evaluate MLOps technologies, ensure security and compliance, conduct design reviews, enforce engineering standards, and mentor junior engineers.

Lead statistical design and analysis for analytical and clinical validation studies of NGS-based cancer diagnostic tests. Develop statistical and machine learning methods, prepare regulatory submissions, ensure analytical quality, collaborate cross-functionally, and mentor junior biostatisticians. Support protocol development, statistical analysis plans, data reporting, regulatory agency interactions, scientific publications, and diagnostic assay development and approval.

Own and expand a portfolio of enterprise health system accounts, serve as executive sponsor, drive retention and growth, build expansion strategies, lead account planning and QBRs, partner with Product and Engineering to accelerate time-to-value, and translate customer needs into product and GTM insights.

Leads Scribd’s content intelligence and UGC supply portfolio, owning corpus classification, metadata, quality, gap analysis, AI infrastructure, contributor experiences, and content acquisition strategy. Partners with ML, engineering, data science, consumer product, and business development teams to connect content supply with business value. Manages senior product leaders and established engineering and analytics programs while setting portfolio roadmaps and representing content strategy with executives.

Own Talon.One’s product narrative, positioning, messaging, competitive intelligence, sales enablement, and go-to-market support for enterprise buyers. Develop collateral, battlecards, solution briefs, launch messaging, ecosystem positioning, and customer-driven insights. Partner with Product, Sales, Marketing, Partnerships, and Customer Success across distributed teams, translating complex technical capabilities—including AI and machine learning—into clear business value for retail and QSR markets.

Seller-doer solution architect who leads technical discovery, architects multi-vendor data and AI platforms, builds proposals and carries quota, then stays hands-on to guide delivery, write production code, and expand accounts across Snowflake, Databricks, cloud, and modern data tooling.

Lead cross-functional development of a low-latency control system for neutral-atom QPUs. Translate technical roadmaps into traceable requirements, define subsystem interfaces, manage testing and integration, coordinate timelines across theory and engineering teams, and convert R&D outputs into product specifications while communicating status and risks to stakeholders.

Lead end-to-end architecture for a major Generative AI platform: define reference architectures, integration contracts, NFRs, and reusable patterns (RAG, model serving, prompt/agent workflows). Partner with product, engineering, data science, cloud, security, and DevSecOps to ensure secure, scalable, compliant, and production-ready AI solutions. Conduct design reviews, proofs-of-concept, and drive platform governance and operational readiness.

This role manages institutional client billing operations, ensuring accuracy in fee calculations, oversight of reporting, and implementation of training programs while leading a team.

Lead and scale the nonclinical safety function for early development programs, define and execute phase-appropriate toxicology strategies, integrate AI/in silico predictions with traditional data, oversee post-IND GLP studies (including chronic, reproductive, carcinogenicity), establish translational biomarker and exposure-based modeling, manage CRO relationships, and lead regulatory strategy and submissions (IND, IB, DSUR, NDA/MAA) including presenting AI-derived safety data to health authorities.

Develop and deploy machine learning solutions for generative AI platforms, LLM-powered applications, and AI toolkits. Interns will solve agentic design and LLM orchestration challenges, prototype and evaluate models, develop pipeline components, and support deployment across the ML lifecycle. The role involves collaboration with ML engineers, product managers, designers, and full-stack engineers, with mentorship and opportunities to work on scalable, production-ready systems.

Lead Kensho's AI partnerships and strategic client portfolio, driving partnership development, cross-functional deal execution, post-sale adoption, and commercial expansion of Kensho's APIs and AI products worldwide.

Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.

Build and improve applied AI models for scientific customer workflows. Responsibilities include model post-training, evaluation loop development, experiment design, debugging model behavior, and integrating AI capabilities into production software. The role collaborates with AI researchers and software engineers to adapt models, create reusable tooling, and translate customer feedback into model improvements.

As a Machine Learning Systems Engineer, you will optimize distributed training systems, enhance performance profiling, and develop high-performance GPUs for machine learning workloads.