Artificial Intelligence Jobs in Santa Clara, CA

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

Manage strategy creation, sales account management, and relationship building for Akamai. Drive revenue growth and improve customer relations in the telecommunications sector.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Serve as a technical advocate connecting Intel AI platforms with global developer communities through presentations, content creation, developer advocacy, ecosystem growth, and feedback to influence product roadmaps and improve developer experience.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

The internship involves multiscale modeling focusing on physics and AI modeling. Interns will run simulations and define workflows using Python and related libraries.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

The Senior MLOps & AI Infrastructure Engineer will architect, build, and operationalize ML systems, design ML pipelines, and manage AI infrastructure while collaborating with various teams on best practices.

The role involves owning the infrastructure for training and deploying RL-based control systems in humanoid robots, enhancing speed, efficiency, and collaboration among teams.

The role involves designing, building, and maintaining AI agents for Roku TV, managing their development lifecycle, and ensuring performance in production environments.

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

As a senior software engineer at NVIDIA, you will design and build tools for performance analysis of AI workloads, lead automation efforts, and enhance workflows for engineers.

Build scalable, resilient agentic AI and LLM/ML solutions. Translate requirements into high-level and detailed architectures. Implement solutions using Python/C++ and ML frameworks, applying AI/ML fundamentals, data structures, algorithms, and distributed systems.

Own product lifecycle for Edge AI suites and tools: define value proposition and requirements, lead cross-functional teams, prioritize features, engage developers and partners, drive adoption, and support launches and marketing for open-source edge AI and robotics solutions.

Provide senior-level Linux system administration and lab operations for HPE HPC & AI performance engineering labs. Tasks include imaging/configuring servers, firmware and switch management, virtualization and high-performance storage (e.g., Lustre), GPU/CPU hardware diagnostics, job-scheduler administration, capacity planning, lab design, and mentoring junior staff while ensuring cybersecurity and operational stability for internal and customer-facing workloads.

Lead product strategy, roadmap, and go-to-market for NVIDIA enterprise game development solutions. Partner with engineering, ISVs, OEMs, and sales to integrate RTX, generative AI, and developer workflows into studio pipelines. Drive positioning, demos, trade-show strategy, sales enablement, and lighthouse case studies to grow adoption among game developers and publishers.

Lead software-hardware co-design to optimize LLM and agentic AI inference. Profile and optimize model execution for memory bottlenecks, influence memory and accelerator architecture, and develop/benchmark inference and serving solutions using PyTorch and vLLM while mentoring teams on best practices.

Contribute to AI research in LLM post-training and reinforcement learning, developing scalable systems for Zoom's AI Companion. Engage in innovative research, implement model architectures, and partner with teams to translate prototypes into production systems.

Design and implement onboard perception systems (computer vision, localization, visual-motor policies, and data annotation), own end-to-end product lifecycle, deliver production-grade software, and collaborate cross-functionally to optimize the autonomy stack for humanoid robots.

Lead and grow a team to design and deliver end-to-end AI hardware/software systems and AI GPU platforms. Define product strategy and multigeneration roadmaps, align cross-functional stakeholders, translate customer insights into requirements, ensure software enablement, and drive execution and innovation across silicon, systems, manufacturing, and go-to-market.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Evaluate AI model outputs, perform audit-based reviews, apply and improve guidelines, identify trends and failure modes, document findings, and collaborate with leads and cross-functional teams to refine quality standards.