Artificial Intelligence Jobs in Santa Clara, CA

Design and build native Android applications for robot data annotation, dataset review, and experiment tracking. Create fast, reliable mobile experiences for real-time data capture, integrate AI models to automate annotation, and collaborate with AI researchers, robot operators, and web tooling teams to ensure consistent workflows.

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

The Director of AI will lead AI and data engineering teams, define and execute AI strategy, and ensure effective partnership across functions, focusing on scalable AI solutions and responsible AI practices.

Design and build scalable EDA flows and AI/LLM-driven automation for 3DIC, interposer, and RDL systems. Implement flow-generation, debugging assistants, root-cause analysis, and platform integration using C++, Tcl, Python, and Java. Collaborate with engineering teams to validate flows, automate constraint-driven designs, and address large-scale, high bump-count and multi-region routing challenges to deliver production-quality solutions.

As a Senior Software Engineer at Tenstorrent, you will develop AI compiler technologies, optimize computational graphs, and enhance MLIR frameworks to connect AI models with hardware.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

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

Senior technical leader advising customers on NVIDIA-powered AI Factories and large-scale GPU infrastructure. Design and validate GPU cloud, Kubernetes, and multi-tenant AI platform architectures; guide networking, security, and operational best practices; lead technical workshops, influence product and sales strategy, and develop repeatable reference architectures and playbooks.

Drive end-to-end LLM inference performance on Intel GPUs: profile bottlenecks, write and optimize custom GPU kernels (attention, MoE, quantization, fusions), upstream improvements to vLLM/SGLang/PyTorch, and collaborate with architecture and compiler teams to shape hardware roadmaps.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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

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.