Top AI Specialist Jobs in San Francisco Bay Area, CA

Build functional AI prototypes and MVPs using low-code platforms such as Copilot Studio and Power Platform. Translate business needs into technical specifications, user stories, and workflows; partner with engineering to scale validated concepts into production. Roll out AI productivity tools, support go-to-market and customer success workflows, measure adoption and impact, and apply AI security, privacy, governance, and compliance principles while coordinating with product, architecture, legal, and security stakeholders.

Review and annotate video assets according to project guidelines, maintain high accuracy and consistency, identify footage quality issues, and report edge cases or contradictions to the AI team. The role also contributes feedback on questionnaire design and process improvements during extended review sessions. This is a part-time, 10-week onsite contract in San Francisco, with possible extension.

Lead CoreWeaves robotics field engineering vertical: design, build, and deploy ML solutions on physical robots using CoreWeave's AI platform; own customer engagements from scoping to production, validate sim-to-real performance, create customer-facing applications, and translate field signals into product requirements.

Annotates data accurately according to project guidelines, performs quality checks, provides feedback on annotation tools and processes, collaborates with project teams, maintains efficiency, and reports issues for resolution.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct comparative experiments, improve response quality, and create reusable prompt libraries. Translate business needs into prompting strategies while collaborating with AI engineers, trainers, product teams, and stakeholders. Document standards, support governance, research emerging techniques, and contribute to Generative AI implementation and continuous improvement.

Supports responsible AI governance by developing frameworks, conducting fairness and risk assessments, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight practices. Works across technical, legal, compliance, risk, and business teams to promote ethical, transparent, accountable, and trustworthy AI systems.

Design, implement, and optimize AI-powered workflow automation solutions. Analyze business processes, document current and future states, gather requirements, integrate automation tools, support testing and deployment, monitor performance, and identify improvements. Collaborate with business and technical stakeholders, provide training and troubleshooting support, promote automation best practices, and contribute to digital transformation and innovation initiatives.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Create precise vector annotations and segmentation masks on geospatial imagery, validate and correct automated detections, maintain documentation and QA, support dataset pipelines, and assist ML teams with inference and model evaluation to produce production-ready training data.

Lead design, validation, and continuous improvement of AI-assisted measurement and learning systems for K-12 assessments. Translate measurement intent into reproducible AI workflows, prototype AI-assisted content generation, ensure outputs meet validity, fairness, accessibility, and instructional standards, design validation and monitoring frameworks, partner with cross-functional teams (AI Science, Technology, Content), and contribute research, documentation, and thought leadership on intelligent assessment and learning systems.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Lead complex, cross-functional AI programs end-to-end: translate strategy into integrated plans, drive multi-team execution, mitigate risks (data, model, compliance), apply ML lifecycle practices, use Agile/kanban methods, manage budgets/vendors, communicate to senior stakeholders, and mentor other program managers to improve AI program standards.

Lead research, design, and deliver Pearson's scalable learner model and AI-driven features. Partner with product, engineering, and design to define data requirements, build prototypes, maintain data pipelines, ensure data quality, and translate research into production ML capabilities and business growth.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Own end-to-end customer onboarding: scope integrations, build and deploy integrations against real customer data, diagnose extraction or pipeline issues, drive adoption and expansion, and act as the technical voice between customers, product, and engineering to shorten time-to-value.

The role involves designing and delivering AI training programs, creating learning materials, and facilitating workshops to enhance user understanding of AI tools within enterprises.

Design, develop, and deploy Agentforce AI agents and custom actions on Salesforce. Build Apex, LWCs, flows, and secure API integrations with legacy federal systems and Data Cloud. Ensure AI safety, compliance, testing, and support CI/CD/DevSecOps practices in cleared federal environments.

Design, develop, and deploy custom ML models and transformer-based NLP systems. Build and maintain ML pipelines, fine-tune LLMs, implement RAG, monitor production performance, research new techniques, and mentor junior engineers.

The AI Adoption Specialist guides organizations in integrating AI technologies, developing strategies, conducting pilots, and overseeing deployment while monitoring performance.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.