Top Machine Learning Jobs in San Francisco Bay Area, CA

Develop AI-enabled software solutions for internal digital transformation initiatives. Design, code, test, debug, and support scalable applications using modern engineering practices. Apply machine learning tools, automate workflows, analyze data, contribute to APIs, microservices, and cloud-native architecture, and translate business requirements into technical designs. Review junior team members’ work, provide technical guidance, and collaborate with internal stakeholders to deliver reliable enterprise applications.

Designs and deploys scalable AI and machine learning systems using Python and TensorFlow. Responsibilities include data wrangling, complex analysis, NLP, deep learning, data pipeline management, data integration, and model optimization. The role also involves stakeholder collaboration, aligning AI initiatives with business goals, mentoring junior team members, and contributing to data infrastructure and engineering practices.

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.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Define technical standards for HPC, GPUs, Kubernetes, private and hybrid cloud, networking, storage, reliability, security, observability, and automation. Solve complex scalability and performance challenges, guide cross-functional initiatives, evaluate emerging technologies, mentor engineers, and embed governance, compliance, privacy, and responsible AI into secure, resilient infrastructure.

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining modeling strategy, designing experiments, analyzing telemetry, influencing senior leaders, validating and stress-testing models, and overseeing the full model lifecycle from design through implementation. The role partners with engineering and research teams and requires extensive experience in large-scale analytics, machine learning, relational databases, cloud computing, and open-source programming.

Develop and improve reliable LLM and deep learning capabilities, build scalable real-time machine learning models, advance Responsible AI practices, and define technical strategies for deep learning, RAG, and LLMs. Collaborate with AI scientists and engineers, conduct ML and reinforcement learning research, fine-tune models, and drive adoption of AI best practices across service teams.

Leads data science strategy for Consumer and Developer Experience, partnering with engineering, analytics, and product teams. Builds and deploys machine learning models across design, training, validation, evaluation, and implementation. Analyzes large-scale numeric and textual datasets using Python, cloud platforms, and open-source technologies, translating complex findings into business decisions and customer-focused digital products.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

Leads Adyen’s Credit Risk Modeling team, overseeing the design, productionization, deployment, monitoring, and improvement of machine learning models and rule-based decision systems for credit underwriting. The manager provides hands-on technical leadership, mentors machine learning scientists and data engineers, shapes architecture and product roadmaps, collaborates cross-functionally, recruits team members, and builds an inclusive, high-performing engineering culture.

Lead the vision, strategy, and roadmap for AI experiences across Atlassian’s core products. Partner with engineering, design, data science, platform, and product teams to deliver intuitive AI features, accelerate adoption, define success metrics, gather customer feedback, and iterate based on data. Serve as an AI product leader, mentor stakeholders, communicate with executives, and drive innovation across a global software portfolio.

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 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.

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.

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 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.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Build and deploy machine learning models for Snapchat products, contributing across data analysis, training, evaluation, and production deployment. Collaborate with cross-functional teams to launch scalable ML-powered features while maintaining high standards for code quality, security, and reliability. Apply established machine learning techniques to ranking, recommendations, search, content understanding, image generation, and related applications.

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.

Build infrastructure and tooling for AI evaluation, including distributed execution, task-set validation, graders, human-review calibration, statistical reporting, online feedback loops, and offline-to-online analysis. Partner with product, engineering, data, and ML teams to create reliable evaluation workflows that support faster, statistically sound AI product decisions.