Artificial Intelligence Jobs in Oakland, CA

Develop and execute go-to-market strategy for AI products: define positioning and messaging, conduct market and competitive analysis, enable sales, coordinate launches, gather customer insights, and measure product performance.

Own new business and account growth across hyperscalers, neoclouds, enterprises, and frontier AI labs in the Americas. Manage complex technical sales cycles, layered OEM/ODM and channel routes, capacity commitments, design-ins, pipeline forecasting, contracts, and executive relationships. Translate semiconductor performance, power, and TCO advantages into commercial outcomes while partnering with product, engineering, marketing, legal, and finance. Build a qualified pipeline through an existing customer network and represent the company at industry events.

Lead Strata from inception as co-founder, defining the vision for regulatory intelligence software, engaging enterprise customers, shaping the product roadmap, providing technical leadership for trusted AI agents, and recruiting teams across go-to-market, product, engineering, and customer success. The role requires early-stage B2B SaaS experience, hands-on LLM and agentic architecture expertise, and the ability to build products emphasizing accuracy, citations, auditability, and expert review.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.

Leads complex government AI deployments from pre-sale discovery through production adoption, overseeing client relationships, delivery planning, cross-functional execution, change management, and executive communication. Translates agency workflows into technical requirements, manages risks and dependencies, drives measurable outcomes, and converts deployment feedback into product improvements. Builds deployment playbooks, operating cadences, and future delivery teams while supporting renewals and expansions. Requires strong AI/ML and enterprise systems fluency, senior stakeholder leadership, and 30–50% travel.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build the enterprise platform for an AI creative product, including workspaces, roles, permissions, SSO, audit logging, usage-based billing, invoicing, and payment infrastructure. Design scalable APIs and multi-tenant architectures, strengthen security and reliability, and create React/Next.js administrative experiences. Partner with founders, sales, and customers to deliver enterprise requirements, establish engineering practices, and help shape the enterprise engineering team.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.

Build, integrate, deploy, and operationalize secure AI solutions across enterprise platforms. Responsibilities include prompt engineering, orchestration, RAG implementation, enterprise data integration, monitoring, performance tuning, documentation, and governance compliance. The role partners with IT, Security, Privacy, Data, and business teams to deliver production-ready AI capabilities using technologies such as OpenAI, Anthropic, Microsoft 365, Salesforce, and ServiceNow.

Own the vision, roadmap, and delivery of e.l.f.'s agentic commerce capabilities across AI assistants, generative search, discovery, personalization, offers, and checkout. Partner with AEO/GEO, Connected Commerce, CRM, engineering, data science, design, and analytics teams to improve product visibility, citations, and conversion. Define requirements, run experiments, measure outcomes, manage risks, and establish agent-friendly commerce experiences and integrations.

Leads AI and legal technology enablement across West Coast law firm offices. Designs training curricula, academies, workshops, communications, and adoption campaigns; manages change initiatives; builds attorney stakeholder relationships; analyzes usage data; reports progress and risks; and promotes responsible, habitual technology adoption among attorneys and practice groups.

Lead cross-functional teams to design and deliver commercial analytics projects for life sciences clients. Provide strategic guidance, analyze data, develop actionable insights, manage client communications and timelines, build/optimize data processes (including coding and visualization), support business development, and mentor junior staff.

Leads technical and writing teams delivering generative AI products, coordinating customers, data, product, and research teams. Tracks annotator metrics, manages labeling initiatives, creates prompts and responses, develops stakeholder guidelines, and trains teams on LLM and dataset best practices. The role includes customer interaction, quality assurance, and exposure to potentially toxic or sensitive content.

Own the AI roadmap for Operations, Finance, and HR systems, identifying and prioritizing automation opportunities across supply chain, inventory, accounting, and people processes. Translate business needs into product requirements, lead pilots and delivery, drive adoption, measure ROI, and communicate outcomes to executives. Partner with engineering, data science, IT, security, and vendors while ensuring AI governance, privacy, compliance, and operational reliability.

Lead US TMT business development with an AI focus: identify revenue opportunities from AI regulation, M&A, governance and data trends; develop client strategies, communications and targeted outreach; support pitches and major US TMT projects; coach and manage MBD colleagues; collaborate with partners and international teams to drive client relationships and revenue growth.

Design, build, and scale AI engines, conversational assistants, context-aware agents, and workflow automations for go-to-market teams. Own the GTM AI roadmap, trusted data and knowledge architecture, AI governance, production monitoring, adoption, and value realization. Partner with Sales, Marketing, Revenue Operations, Product, and Systems leaders to translate business needs into scalable AI solutions and measure their impact.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Build foundational AI-enabled applications, prototypes, prompts, tests, documentation, and agentic automations. Translate defined requirements into software components, scripts, integrations, and workflow configurations. Support prompt engineering, retrieval-assisted use cases, API integrations, testing, troubleshooting, documentation, code reviews, and demonstrations. Apply software development lifecycle practices, data privacy, information security, compliance, and responsible AI standards while learning from senior team members.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Lead research to develop post-training reinforcement learning methods that convert high-throughput biological measurements into rewards for generative biological language models. Architect and run large-scale model training, work closely with experimental teams to align measurements and modeling, and iterate rapidly to publish new methods and models.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Support hydrogeology, water resources, hydraulics, and geotechnical projects through data collection, modeling, analysis, visualization, field work, and client communication; assist with proposals, project management, and business development under supervision.