Top Machine Learning Jobs in San Francisco Bay Area, CA

Leads the design, development, and deployment of agentic AI, generative AI, NLP, and machine learning models for consumer banking applications. Translates business needs into scalable technical solutions, guides data scientists, collaborates with business and technology teams, communicates model outcomes, and ensures regulatory and risk compliance. The role focuses on virtual assistants, content generation, agentic frameworks, operational efficiency, customer and employee experience, and risk mitigation.

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.

Analyze large datasets using quantitative analysis, data mining, statistical modeling, metrics, dashboards, and reports to generate actionable product and business insights. Collaborate with product managers, engineers, marketers, and designers to improve product decisions and adoption. Develop scalable data solutions using AI tools while maintaining statistical rigor, and independently drive analytical projects to completion.

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 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.

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.

Build infrastructure and tooling for AI evaluations, including distributed scoring engines, task-set validation, grader systems, human-review calibration, statistical leaderboards, online feedback capture, and offline-to-online evaluation loops. Partner with product, engineering, data, and ML teams to deliver reliable, observable evaluation workflows that support faster and more trustworthy AI product development.

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.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Commercial Data Scientist will develop machine learning models, analyze large healthcare datasets, identify clinician behavioral patterns, and create reusable data products. The role partners with Product, Data, Strategy, Insights, and Sales teams to deliver client-facing analyses and data-driven recommendations. Responsibilities include statistical analysis, production model development, complex SQL querying, scalable data processing, automation, and communicating technical insights through compelling visualizations and narratives.

Leads enterprise data science initiatives for agentic AI products, including AI/ML model development, experimentation, telemetry analysis, and strategic recommendations for senior leadership. Partners with data scientists, engineers, and product managers while translating complex statistical insights into business outcomes. Requires extensive experience in quantitative analytics, large-scale data analysis, production AI/ML deployment, A/B testing, and team leadership.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.