Machine Learning Jobs in Cambridge, MA

Design, build, and scale production machine learning and data pipelines for fraud, payment risk, and abuse detection. Develop systems spanning feature engineering, model training, deployment, monitoring, and observability. Collaborate with data scientists, engineers, and product managers to productionize models and maintain reliable decisioning systems. Participate in code reviews, incident response, on-call rotations, and post-incident improvements while mentoring junior engineers and contributing to shared tooling and documentation.

Advise clients on privacy, data protection, and responsible AI requirements. Evaluate AI systems for fairness, transparency, safety, privacy, accountability, and third-party risk. Develop governance operating models, policies, risk-tiering approaches, controls, impact assessments, and client deliverables. Interpret regulatory changes, support breach investigations, collaborate with legal, compliance, cybersecurity, data, and technology teams, and mentor junior staff. Apply technical literacy in AI/ML, GenAI, software development, technical architecture, and Python.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Develop and deploy AI capabilities across enterprise ERP platforms including Workday and SAP Ariba. Build intelligent agents, chatbots, automation, integrations, and custom GenAI/LLM solutions. Establish PaaS environments, cloud services, deployment pipelines, security, governance, and engineering processes. Lead AI projects from concept through production, evaluate vendor versus custom solutions, support technology roadmaps, and promote AI literacy. Provide production support and training while aligning solutions with business needs and measurable ROI.

Leads Pfizer’s R&D Clinical Creation Center, owning digital, data, and AI capabilities across the clinical trial lifecycle. Establishes technology strategy, manages a significant budget, modernizes eClinical platforms, embeds AI and analytics into trial delivery, and shifts teams toward product-oriented Agile models. Partners with clinical, scientific, and R&D leaders while building a high-performing cross-functional organization and maintaining regulatory, quality, and compliance standards.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads data science work for personalized recommendation systems, partnering with engineers and product managers to deliver customer-facing products. Builds and deploys machine learning models across design, training, evaluation, validation, and implementation using large-scale data. Applies advanced methods including recommender systems, reinforcement learning, causal inference, transformers, and foundation models. Translates complex analyses into business outcomes while developing talent and guiding technical strategy.

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

Leads Takeda’s next-generation biologics strategy and programs from discovery through candidate selection and IND-enabling development. Builds multispecific biologics, protein engineering, AI-enabled design, computational biology, automation, and high-throughput experimentation platforms. Integrates discovery, translational, CMC, regulatory, clinical, and business functions while advancing portfolio value. Recruits and develops multidisciplinary scientific talent, establishes governance and partnerships, and drives measurable improvements in discovery productivity, differentiation, decision quality, and pipeline success.

Develop deep learning models for structure prediction, protein-ligand co-folding, affinity prediction, and generative molecular design. Build rigorous benchmarking pipelines, analyze structural and sequence datasets, and integrate validated models into drug discovery workflows. Collaborate with ML engineers, structural biologists, medicinal chemists, and software engineers while communicating findings through presentations, technical writing, and publications.

Leads Takeda’s global small-molecule in vitro pharmacology function, setting scientific strategy, building distributed teams, and overseeing assay development, automation readiness, high-throughput screening, translational pharmacology, technology investments, budgets, metrics, and external scientific engagement. The role supports discovery through candidate selection and IND-enabling decisions while integrating pharmacology with ADME, DMPK, biomarkers, AI, and other cross-functional capabilities.

Leads demand forecasting and commercial assessments for plasma-derived therapies, including pipeline and lifecycle opportunities. Develops quantitative forecasts, business cases, market analyses, and executive recommendations. Coordinates cross-functional partners, manages forecasting models and vendors, facilitates leadership discussions, and improves forecasting processes using advanced analytical methods such as regression, optimization, and Monte Carlo simulation.

Owns Takeda’s AI-native small-molecule discovery product family, including scientific vision, product strategy, roadmap, adoption, and value realization. Leads cross-functional teams across drug discovery, computational science, data, technology, and external partners. Defines AI-enabled workflows, priorities, KPIs, and technology direction while ensuring products meet scientists’ needs and deliver measurable scientific and business impact.

Leads Takeda’s AI-native large molecule discovery product family, owning scientific vision, product strategy, roadmap, adoption, KPIs, and business value. Partners with scientists, computational teams, technology leaders, development squads, and external vendors to redesign biologics discovery workflows using AI, data, and automation. Provides portfolio-level direction, prioritizes capabilities, aligns stakeholders, guides technology choices, and drives measurable scientific and business impact across the biologics research organization.

Owns the strategic vision, roadmap, delivery, adoption, and business value of Takeda’s AI-native discovery automation and robotics product family. Leads cross-functional teams across scientific, digital, data, computational, automation, and technology functions; defines AI-enabled laboratory workflows; prioritizes investments; guides platform and integration choices; manages stakeholders and external partners; and drives measurable improvements in laboratory throughput, data quality, scientific impact, and operating efficiency.

Leads AI/ML application to large-molecule drug discovery, including antibody, bispecific, ADC, and other biologics design. Develops generative, predictive, and optimization models; manages large biological datasets; integrates computational methods with automated Design-Predict-Make-Confirm experiments; and partners with cross-functional drug development teams. Provides strategic direction, mentors scientists, identifies portfolio opportunities, and communicates technical insights to senior stakeholders.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Leads data science initiatives for customer financial resiliency, partnering with engineers and product managers. Builds, validates, implements, and productionizes supervised and reinforcement learning models across large-scale data. Uses Python, cloud platforms, open-source machine learning tools, and statistical methods to deliver personalized customer solutions. Translates complex analyses into business outcomes, evaluates emerging technologies, and develops team talent.