Artificial Intelligence Jobs in Sunnyvale, CA

Design, build, fine-tune, and deploy Generative AI solutions using LLMs (Python). Preprocess unstructured data, apply prompt engineering, RAG, and vector DBs, deploy and monitor models/APIs, and collaborate cross-functionally while following engineering best practices.

Lead and manage AI software product launches, collaborating with cross-functional teams and ensuring high-quality delivery while utilizing program management best practices.

As a Full-Stack Engineer at Labelbox, you will develop and maintain AI tools, focusing on data training systems, intermediate user interfaces, scalable architectures, and work in a collaborative, fast-paced environment.

As a Staff AI Software Engineer, you will design and build autonomous AI systems for security testing, focusing on reliability, scaling, and integration with enterprise infrastructure, all while collaborating with top security experts.

Lead the AI Evaluation module for Harness, defining vision and strategy, managing P&L, and enhancing AI application validation and performance in production.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Designs and oversees development of Intel GPU AI software across the entire stack, ensuring performance, stability, and ease of use, while mentoring technical leaders and aligning technical strategies with organizational goals.

Lead UX and product design for a next-generation surgical data and AI platform. Drive user research, define multimodal UX paradigms, build design systems, lead a team of designers, and partner with product, engineering, data science, and clinical stakeholders to deliver compliant, secure, enterprise-scale AI-enabled solutions.

Develop AI compiler solutions, optimize GPU programming, build training pipelines, collaborate on integrating AI policies, and conduct rigorous performance benchmarking.

Lead and mentor a team of 4-8 engineers to build and evolve AI developer tools for local and cloud GPU workflows. Define roadmaps with product partners, manage cross-team dependencies, drive technical execution, improve engineering efficiency via tools and automation, and ensure security and compliance.

Design and implement APIs, CLIs, and application logic for AI developer tools. Drive tech stack decisions, ensure cross-team consistency, lead CI/test automation, and collaborate with UX, frontend, and AI experts. Support, maintain, and document software.

The role involves deploying AI networking solutions in data centers, driving architectural decisions, troubleshooting network issues, and collaborating on customer projects for NVIDIA's technology solutions.

Designs and implements generative AI agents and platforms using local and cloud LLMs. Builds agent orchestration, RAG workflows, vector search integrations, and production-quality software (implementation, testing, debugging, documentation, deployment). Collaborates with stakeholders to deliver AI-powered solutions.

Own the full enterprise sales cycle for AI solutions: identify and acquire B2B clients, analyze needs, design tailored AI solutions with technical teams, negotiate and close contracts, track delivery, and meet revenue targets while conducting market and competitive analysis to inform strategy.

Build and scale backend infrastructure for Ambient AI in healthcare. Work across real-time clinical documentation, AI-driven billing automation, and clinical intelligence. Ship production systems, collaborate with clinicians and customers, maintain reliability, monitoring, and metrics, and operate across the full software stack.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

The Solutions Architect will design and build enterprise AI solutions, aid in adopting NVIDIA AI technologies, and work on deep learning applications for customers across industries.

Develop and optimize high-performance Vision AI systems, pipeline implementations, and collaborative projects, leveraging computer vision and deep learning for real-world applications.

Senior QA Automation Engineer responsible for designing and executing manual and Python-based automated tests for a Network AI platform. Simulate complex multi-vendor network topologies, validate AI/ML correlation engines, WebUI, REST APIs, CLI, syslog processing, NLP pattern recognition, multi-cloud and ITSM integrations, and maintain PyTest-based automation frameworks while reporting and verifying bug fixes across releases.

Seeking a Machine Learning Compiler Engineer with expertise in compiler technologies, machine learning, and software engineering to innovate in development tools and AI solutions.

The role involves architecting and optimizing AI inference systems, developing GPU kernels, and contributing to benchmark methodologies, requiring substantial experience in performance engineering and various programming technologies.

The AI Hardware Architect will analyze and design next-generation AI hardware, focusing on microarchitecture, connectivity, and system performance for GPUs and CPUs.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.