Top AI Specialist Jobs in San Francisco Bay Area, CA

Leads CoreWeave’s AMER inference market strategy, driving adoption of Serverless and Dedicated Inference offerings. Engages customers on AI runtime architecture, serving frameworks, performance, latency, throughput, cost-per-token economics, and deployment tradeoffs. Shapes product and engineering roadmaps through customer feedback, develops technical playbooks and benchmark narratives, supports complex sales opportunities, and builds repeatable go-to-market motions for production-scale AI workloads.

Operates and supports AI solutions, automation workflows, and knowledge tools for a law firm. Responsibilities include monitoring performance, troubleshooting issues, maintaining prompts and knowledge sources, reviewing AI outputs, coordinating releases and enhancements, managing feedback and backlogs, analyzing adoption metrics, maintaining SharePoint and knowledge repositories, improving data quality, and supporting legal, market, and technology research.

Develops AI/ML algorithms, deep-learning models, agentic workflows, and physics-informed computational methods for drug discovery. The role evaluates predictive approaches, integrates scientific data and simulations, supports molecular design and experimental prioritization, builds reproducible workflows, collaborates with chemistry teams, publishes research, and mentors scientists.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Shape conversational AI behavior by writing prompts, creating and annotating dialogue datasets, automating content workflows, testing model versions, evaluating responses, and identifying whether issues stem from prompts, datasets, or models. Collaborate with ML engineers to improve output quality, analyze user conversations, and maintain standards for tone, style, explicitness, and quality. The role requires strong English writing, hands-on LLM experience, analytical ability, and comfort with adult content.

Develop and deploy Microsoft Cloud solutions using Cloud Foundry, AI, Agentic AI, and Copilot technologies. Collaborate with cross-functional teams, optimize automation and integrations, lead technical design discussions, resolve deployment challenges, monitor industry trends, and provide technical training and mentorship.

Freelance AI Specialist responsible for completing and maintaining a shared business knowledge base, improving AI workflows with Claude and Gemini, creating reusable prompts, connecting AI to business tools, documenting processes, training teams, and reporting progress. The role requires careful fact-checking, strong written communication, confidentiality judgment, basic scripting or automation skills, and self-directed project execution.

Designs, builds, and evaluates complete AI systems for Pearson’s K-12 assessment platform. Responsibilities include predictive machine learning, statistical methods, retrieval, knowledge graphs, generative and agentic AI, deterministic services, human review, and system integration. The role develops evaluation datasets and regression suites, analyzes failure modes, supports model adaptation, monitors deployed systems, and collaborates with engineers, psychometricians, learning scientists, and content experts to create transparent, reliable, and auditable AI capabilities.

Design and build Pearson’s knowledge architecture for AI-powered K-12 assessment and learning systems. Responsibilities include developing knowledge graphs, ontologies, schemas, data pipelines, APIs, retrieval and context architectures, and hybrid AI integrations. The role establishes provenance, versioning, traceability, quality measures, and responsible AI practices while partnering with engineers, AI scientists, psychometricians, learning scientists, and content experts. It requires autonomous innovation, rapid prototyping, rigorous evaluation, and translating educational and assessment expertise into computational representations.

Designs, builds, and scales AI-enabled tools, agentic workflows, analytical components, and data innovation frameworks for clinical, real-world, and operational data. Partners with scientists, engineers, product teams, and external collaborators to pilot emerging technologies, evaluate unconventional data sources, and support scientific decision-making. Provides innovation expertise and mentorship while ensuring traceability, human oversight, data privacy, security, and regulatory compliance.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.

Reconstructs and redesigns data-driven presentations in Microsoft PowerPoint, creating visualizations and analytics reports that communicate complex insights clearly. Reviews and edits content for accuracy, flow, and narrative impact; applies data analysis and statistical reasoning; and refines slides based on feedback. The role supports AI training projects through high-quality presentation and analytical deliverables.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.

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.

Designs, builds, and optimizes AI-driven automation workflows that reduce manual work and improve business efficiency. Responsibilities include integrating CRM systems, APIs, and automation platforms; developing chatbots, AI assistants, and prompt systems; monitoring performance; troubleshooting errors; and collaborating with marketing, operations, and technology teams. The role also requires staying current with AI tools and automation trends.

Perform object detection annotation on visual datasets using digital annotation tools. Label and review objects, apply project guidelines, correct inconsistencies, maintain data quality and confidentiality, and meet productivity standards. The role requires accuracy during repetitive tasks, independent remote work, collaboration with project teams, and a consistent 40-hour weekly schedule.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

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.

Annotate construction drawings using bounding boxes, polygons, and segmentation masks for computer vision models. Maintain consistent, accurate labels, clean datasets, identify ambiguous cases, and help improve annotation guidelines. Meet weekly quality and throughput targets while communicating data issues to ML engineers. The role requires focused repetitive work, precise instruction-following, independent remote work, and clear written English. It is a remote, 3–6 month contract with potential full-time conversion.

Design and implement technical AI governance frameworks for generative and agentic AI systems. Assess ethical, safety, security, and sociotechnical risks; evaluate architectures, data flows, permissions, tool integrations, and human oversight; and develop practical mitigations and controls. Partner with engineering, product, legal, security, and business teams to embed responsible AI practices, conduct red teaming and risk assessments, support regulatory alignment, and advance AI literacy across the organization.

Supports responsible and scalable AI adoption by coordinating intake, inventory, governance documentation, risk reviews, approvals, vendor and regulatory records, project milestones, training, monitoring, reporting, dashboards, workflows, and issue escalations. Partners with clinical, operations, compliance, HR, IT, security, EMR, analytics, and finance teams to enable safe, compliant AI deployment across the organization. Travel to satellite and other locations is required.

Designs and demonstrates computer vision solutions for customer opportunities, including model selection, data strategy, evaluation, edge deployment, and business-case development. The role leads discovery, high-level architecture, proofs of concept, demos, partner engagement, and technical enablement. It requires hands-on expertise across classical computer vision, deep learning, video intelligence, vision language models, model lifecycle management, and edge optimization, plus strong customer-facing and commercial communication skills.

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.

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.