Artificial Intelligence Jobs in Santa Clara, CA

Design, build, deploy, and operate AI-enabled backend services and workflows for risk decisioning. Combine LLMs, retrieval, tool use, and deterministic logic with robust monitoring, fallback strategies, and production readiness. Partner cross-functionally to define quality bars, evaluate AI outputs, and improve reliability, scalability, security, and cost efficiency.

Lead synthesis, placement, routing, and full-chip physical design for high-speed AI silicon. Perform block and full-chip physical verification, timing closure, power/performance/area optimization, DRC/LVS and IR/EM resolution, STA and constraint drafting, floorplanning, and script-based flow enhancements. Collaborate with RTL teams to integrate blocks into the full-chip architecture.

Owns end-to-end technical programs for site and data center operations supporting AI cloud and customer deployments. Single-threaded owner across hardware, AI cloud, network/storage, and facilities; drives site readiness, installation, commissioning, incident reviews, KPIs (availability, MTTR/MTTD, capacity), executive dashboards, governance, and risk tracking.

Lead Cerebras's model portfolio: decide which models ship, set quality standards and benchmarks, partner with model labs and open-source maintainers, drive model launches, enable customers, prioritize optimizations, and coordinate cross-functional teams to maximize inference performance and adoption.

Architect and drive a unified, self-service inference control plane and reliability practices for large-scale multi-datacenter and cloud inference fleets. Build capacity orchestration, rollout safety, observability, SLO-based reliability, incident response, and automation; mentor senior SREs and measure impact through reduced toil, faster deployments, and SLO compliance.

Own realtime product marketing for Cerebras inference: create technical content, benchmarks, demos, and influencer/community programs; set editorial direction; identify competitive edges and drive organic growth among developers and AI builders.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

Design, build, and operate agentic AI workflows and MCP servers for an LLM-based network operations assistant. Implement LangGraph orchestration, RAG/memory systems, evaluation frameworks, and production integrations (Kubernetes, CI/CD, containers, FastAPI). Collaborate with domain experts, ensure observability, and drive reliability of non-deterministic AI systems at scale.

Own strategy and execution for a portfolio of AI-native and hyperscale customers. Serve as primary relationship owner, drive revenue, market share and product adoption, coordinate with solution architects, product marketing, OEMs and partners, forecast and train customers, and evangelize NVIDIA platforms to accelerate enterprise growth.

Lead product strategy and lifecycle for local AI system software, tools, and apps. Drive customer discovery, define requirements, coordinate cross-functional teams, set KPIs, and support go-to-market to increase adoption of local AI solutions.

Lead design and delivery of SMAC workflow methodology and infrastructure to keep system specifications and manufacturing test specs aligned. Build production-grade Python pipelines, automated checks, CI gates, and agent-ready tooling that detect spec drift pre-silicon. Integrate workflows into program milestones, drive cross-org adoption across design, operations, and DFX, and apply AI where appropriate while ensuring reviewable, auditable artifacts.

Work as an early-career full-stack software engineer building and maintaining web application features across backend, frontend, APIs, data, tests, and internal tooling. Use AI-assisted development tools responsibly, write maintainable code and tests, participate in code reviews, and learn product and customer context to deliver production-quality outcomes.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Own the full product loop: identify high-impact problems, rapidly prototype and ship AI-accelerated solutions, build internal tools and automations, collaborate with operations and business teams, and iterate based on usage and data to drive adoption and business impact.

Research and develop GPU-accelerated techniques for AI and financial workloads. Analyze and optimize performance on CPU/GPU architectures, collaborate with industry and academia, publish findings, and influence hardware and software design while supporting developer adoption.

Design and implement AI-native frameworks, optimize performance for deep learning models, and ensure high-quality software development using modern C++ and AI tools.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

As a Senior Software Architect for AI Networking, you'll design and implement AI networking technologies, optimize data processing in AI systems, and collaborate on cutting-edge solutions.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The intern will design and implement AI-driven automation workflows to enhance the silicon testing process, collaborate with engineers, and document automation recipes.

Lead and inspire a small team in AI research focused on autonomy and knowledge capture related to semiconductor equipment. Responsible for project roadmap and development of software modules.

Drive product strategy and execution for Cloud Provider and AI Networking segments. Collaborate with teams to define product requirements, assess market opportunities, and develop go-to-market strategies for Tier 1 and Tier 2 customers.

Assist in deploying and monitoring Cerebras AI infrastructure, perform troubleshooting, collect telemetry, and learn through shadowing senior engineers.

Lead the development and management of AI infrastructure, ensuring optimal performance of systems and providing mentorship to engineers while deploying advanced AI models.

Design, develop, and productionize scalable AI tools for systems engineering in autonomous vehicles, integrating data for traceability, and building backend services.