Top AI & Machine Learning Jobs in San Francisco, CA

Leads architecture and delivery of AI-powered pharmaceutical products, establishing AI-first engineering, testing, evaluation, LLMOps, security, and auditability practices. Partners with product managers and client technology stakeholders, oversees offshore data science, engineering, QA, and DevOps teams, and makes scalable platform decisions. Also directs Adobe-focused analytics initiatives involving data pipelines, statistical modeling, predictive analytics, visualization, KPI frameworks, and executive recommendations.

Leads multidisciplinary teams delivering AI-enabled, cloud-native applications on AWS. Oversees architecture, development, testing, deployment, modernization, platform engineering, security, reliability, and client advisory activities. Translates business objectives into technical roadmaps, guides complex engineering work, coordinates cross-functional architecture reviews, communicates delivery risks and options, and coaches teams across multiple workstreams.

Designs, builds, tests, and troubleshoots full-stack cloud-native applications on AWS. Translates client needs into technical solutions across architecture, development, testing, and delivery; builds scalable infrastructure and integration patterns; applies AI-enabled engineering methods; and supports modernization initiatives. The role also reviews technical work, mentors junior engineers, collaborates with architecture teams, and helps deliver cloud, data, and analytics solutions.

Leads AWS-native application design, development, testing, modernization, and architecture efforts. Manages technical workstreams, delivery timelines, scope, and team allocation while coaching engineers on cloud infrastructure, AI-native engineering, testing, and troubleshooting. Reviews technical designs and code, coordinates application and infrastructure activities, supports client architecture discussions, and improves delivery reliability and speed.

Leads AI-enabled risk modeling services, setting strategy, managing client relationships, overseeing machine learning and predictive analytics solutions, and guiding end-to-end delivery. The role includes model governance, feature engineering, performance evaluation, innovation, cross-functional collaboration, team coaching, thought leadership, and business development across risk modeling engagements.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.