Artificial Intelligence Jobs in Sunnyvale, CA

Provide expert legal and business research to attorneys and staff using advanced research tools; advise on research strategy; manage research workflow; develop training, onboarding, newsletters, and intranet content; evaluate and recommend online resources; support continuous learning and Research Services initiatives.

Design and deploy generative AI workflows and multi-agent architectures for healthcare applications. Curate EHR and claims data, extract clinical variables from unstructured medical text, and develop evaluation pipelines for agent safety, accuracy, factuality, and compliance. Apply advanced machine learning, NLP, and LLM techniques to create trustworthy longitudinal datasets supporting autonomous care agents and clinical decision tools. Communicate technical findings to technical and non-technical stakeholders.

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.

The Senior Manager of Embodied AI will oversee AI/ML engineering teams, manage operational excellence in data scaling for autonomous vehicles, and collaborate across teams to improve performance and innovation in AV technology.

Builds developer and customer relationships across the Jetson ecosystem, guides partners through embedded Linux and Jetson software integration, promotes NVIDIA AI libraries and edge AI capabilities, supports production deployments, and communicates developer feedback to engineering and product teams to shape platform roadmaps, reference designs, positioning, and adoption.

Build and own mobile experiences for clinicians using Flutter, Swift/iOS, and Kotlin/Android. Develop AI-native workflows involving real-time transcription, ambient documentation, multimodal inputs, offline functionality, and intelligent recommendations. Collaborate with clinicians, product, design, AI, and backend teams from discovery through launch. Ensure performance, accessibility, reliability, analytics, testing, observability, and release readiness while contributing to mobile strategy and mentoring engineers.

Leads and scales OpenAI’s Applied AI Engineering team supporting startup customers. Defines the operating model, develops technical customer-facing engineers, advises startups on architecture and production deployment, partners with Sales and cross-functional teams, and turns recurring customer needs into repeatable playbooks, tooling, reference architectures, and deployment mechanisms. Serves as a senior escalation point while balancing customer impact, product priorities, platform strategy, safety, and team growth.

Lead and grow a team of AI Deployment Engineers to design, deploy, and scale secure, production-grade AI systems for enterprise customers. Own technical success across architecture, reliability, security, adoption, and expansion; act as senior technical escalation; partner cross-functionally to create reusable patterns, operating models, measurement, and product feedback loops while hiring and developing talent.

Design and build AI agents, first-party web and mobile apps, and scalable backend services. Integrate LLM frameworks and enterprise systems, implement APIs and microservices, ensure observability and security, and collaborate with Finance, Security, and Engineering stakeholders.

Lead and grow a technical team responsible for verifying AI compilers for next-generation workloads. Define formal verification requirements, drive AI-assisted and compiler-aware verification (fuzzing, differential testing, symbolic reasoning, formal methods), and partner with compiler, CUDA, runtime, and ML framework teams to build scalable verification infrastructure and improve production confidence.

Build and test Marvis Minis across embedded firmware and cloud services. Develop Minis features, author network/application validation tests, debug cross-layer issues spanning AP firmware to cloud data pipelines, extend platform to additional devices, and maintain cloud processing components (Storm, Airflow, Kafka, Redis, Elasticsearch). Collaborate with firmware, data science, QA, and operations and participate in production rollouts and incident response.

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.

Manage ELEVATE cohorts, workshops, pilots, and participant relationships while sourcing startups, coordinating pilots, and supporting fundraising through grants and sponsorships. Track impact, prepare reports, and handle event logistics and partner engagement to grow SMB innovation programs.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Architects data models and pipelines that transform multimodal surgical and robotic data into research-ready AI datasets. Owns data collection strategy, quality frameworks, validation, anomaly detection, lineage, annotation workflows, and dataset tooling. Partners with AI researchers, surgeons, clinicians, and robotics engineers to capture relevant signals and support intelligent surgical system development. Contributes to embodied AI infrastructure and data strategy.

Partners with customers and internal teams to design, deploy, and optimize generative AI and LLM inference solutions. Develops proofs of concept, supports NVIDIA technology adoption, analyzes AI workload performance and power efficiency on Kubernetes, and advises developers, researchers, and data scientists. The role requires expertise in deep learning frameworks, GPU orchestration, MLOps, containerization, observability, and LLM inference, with occasional travel to conferences and customers.

The Technical Solutions Engineer resolves customer issues, troubleshoots complex network environments, and collaborates with engineering teams to ensure customer satisfaction.

The Solutions Architect will implement ERP projects focusing on NetSuite integration, optimize functionalities, and ensure system reliability while collaborating with stakeholders.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.

Design and deploy scalable AI inference architectures for strategic partners. Build and operate GPU inference recipes, optimize pipelines using leading inference backends, and guide disaggregated serving deployments. Evangelize Kubernetes, compute fabric, DevOps, and observability practices while mentoring customers and internal teams. Lead technical engagements, resolve complex infrastructure issues, create reference architectures, and provide product insights. The role also involves agent design, sandboxing, memory and retrieval systems, evaluation, skill governance, and model customization.

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.

Apply deep battery-systems expertise to define SOC/SOH/thermal states and BMS logic, incorporate safety constraints, design agentic AI decision frameworks, analyze large-scale vehicle and telematics data with Python, and collaborate across BMS, data science, and AI teams to develop adaptive battery management strategies.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

PhD-level applied science intern role to design, train, and scale ML models (search, ranking, recommendation, retrieval, NLP, CV). Responsibilities include dataset curation, building ML/data pipelines, running experiments and statistical evaluations, and collaborating with engineers and product teams to productionize AI solutions using cloud technologies.

Lead the Solutions Architecture team, managing complex implementations of AI solutions, mentoring technical staff, and aligning customer needs with product roadmaps.