Top AI Specialist Jobs in San Francisco Bay Area, CA

Annotate 3D wire spans and identify visualization errors in LiDAR/camera datasets using internal tools. Maintain data integrity, document anomalies, collaborate with engineers, QA, and field techs, and deliver accurate datasets for utility infrastructure projects.

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Leads applied AI risk standards for areas including mental health, youth safety, age assurance, reliability, efficacy, and privacy. Translates research, policies, and evaluation methods into measurable standards, test methods, assessment criteria, and assurance requirements. The role involves drafting proposals, negotiating with standards bodies, coordinating internal stakeholders, representing OpenAI externally, and contributing to frontier AI risk-management and independent-assessment standards.

Lead go-to-market and technical strategy for AI developer tooling, winning early customers and building repeatable playbooks. Translate customer MLOps requirements into product feedback, partner with engineering and Weights & Biases, and enable sales/solutions teams to close deals around experiment tracking, model governance, and LLM observability.

Drives AI adoption across a regional Wealth Management field organization. Builds relationships with advisors and leadership, translates AI capabilities into practical workflows, delivers training and coaching, supports product and technology rollouts, identifies business development opportunities, gathers feedback, and develops strategies to sustain adoption across client engagement, prospecting, campaigns, and growth activities.

Evaluate LLM and automation outputs for accuracy, consistency, and reliability; create annotations and training datasets; research ambiguous cases; support product recognition and classification models; and use AI tools to improve operational efficiency. The role also involves maintaining model and catalog quality through mapping and classification work.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Designs, builds, tests, and deploys AI-enabled automations and copilots for GTM and RevOps. Creates reusable agent templates, prompt libraries, and orchestration patterns; implements end-to-end API integrations; monitors performance; documents and hands off production solutions; and trains/coaches business teams on safe, responsible AI usage and prompting best practices.

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.

Architects, develops, and operationalizes production-grade agentic AI systems using LLMs and Model Context Protocol frameworks. Builds multi-step, tool-using agents, orchestration frameworks, cloud integrations, and secure AI applications. Leads solution design, proof-of-concept innovation, and delivery across engineering and product teams. Requires strong Python, distributed systems, cloud, LLMOps/MLOps, CI/CD, and software engineering expertise, with U.S. citizenship and eligibility for Public Trust clearance.

Lead and establish CoreWeave's Autonomous Vehicles field engineering vertical: design, build, validate, and deploy ML solutions and customer-facing applications using CoreWeave products. Drive complex customer engagements, translate field signals into product requirements, and set technical standards for perception, motion planning, simulation, and safety-focused AV development.

Configure and improve AI agents for enterprise procurement workflows. Translate customer requirements into prompts, rules, and configurations; test edge cases; investigate unexpected AI behavior; and refine solutions. Work directly with customers to clarify needs, build reusable templates, and share insights with product and engineering teams. The role emphasizes precise writing, problem-solving, technical curiosity, rapid learning, and ownership rather than prior software engineering or AI experience.

Build and scale agentic AI systems that automate customer experience workflows across Slack and Salesforce. Analyze transcripts, detect SLA risks and escalations, provide self-service operational insights, and develop AI memory architecture. Partner with data, QA, workforce management, and customer advocacy teams to ensure system accuracy, reliability, trustworthy outputs, and measurable operational impact. Productize internal tools and design human escalation patterns for scalable deployment.

Own customer AI implementations from discovery through launch and ongoing optimization. Configure LLM workflows, design prompts, build evaluations, run regression testing, troubleshoot production issues across integrations and APIs, and coordinate resolution with customers and internal teams. The role requires clear communication, technical investigation, customer testing facilitation, and improving AI reliability in production.

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.

Design and deploy production ML/AI architectures on the Databricks platform, lead Generative AI (LLM, RAG, agent) implementations, optimize and scale MLOps pipelines, provide technical pre-sales support, and translate customer feedback into product improvements while mentoring peers and meeting delivery milestones.

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.

Designs, implements, optimizes, and administers generative and agentic AI solutions. Translates business requirements into AI workflows, oversees model deployment and performance, analyzes implementation data using Power BI, and coordinates responsible AI governance. Collaborates with engineering, security, data science, sales, and reporting teams while supporting users and maintaining AI platforms, cloud services, and low-code tools.

Reviews videos of robotic arms, identifies key actions and outcomes, and applies detailed grading guidelines to annotate events. Uses video annotation platforms to create structured datasets, collaborates with trainers to resolve ambiguities, documents findings, communicates progress, and participates in quality reviews. Strong attention to detail, organization, communication, and accuracy are essential. Experience with video annotation, AI training, machine learning data preparation, or robotics is preferred.