Artificial Intelligence Jobs in Santa Clara, CA

Lead architecture and implementation of AI-security products, define technical direction, drive cross-team initiatives, design secure scalable backend systems, and address systemic risks while remaining hands-on in coding, prototyping, and debugging.

This role involves developing and implementing testing frameworks for ensuring reliability and performance of AI security controls across platforms. The engineer will expand test coverage and contribute to quality metrics for AI models.

Leads SSD enablement, qualification, validation, and production readiness for NVIDIA, AMD, and other AI platforms. Coordinates customer and internal engineering teams to resolve hardware, firmware, PCIe/NVMe, thermal, power, performance, and interoperability issues. Identifies emerging AI storage workloads, conducts feasibility studies and proof-of-concepts, evaluates ecosystem trends, and develops future product opportunities.

Architects and delivers enterprise integration and API platforms using event-driven, asynchronous, cloud, and AI-augmented workflows. Develops reusable frameworks across SAP, SaaS, and partner ecosystems; governs APIs; integrates EDI, REST, GraphQL, and SOAP systems; and builds autonomous, self-monitoring and self-healing workflows. The role also requires programming, identity and access control expertise, CI/CD automation, observability, and close collaboration with Product and Engineering teams.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Manage major infrastructure projects in the energy sector focusing on electric transmission and substation upgrades, ensuring quality and stakeholder satisfaction.

Develop and maintain end-to-end LiDAR and camera perception software for robotics platforms. Responsibilities include sensor drivers, ROS 2 packages, GPU-accelerated processing, calibration, synchronization, recording and playback tools, Docker environments, packaging, CI/CD, hardware-in-the-loop testing, and production release automation. The engineer will support integration issues and collaborate closely with hardware, silicon, algorithm, manufacturing, and customer teams.

Serve as senior technical advisor to strategic partners for Arm-based Edge AI SoC solutions. Lead system-level architecture, IP selection, integration, optimization, technical engagements, demonstrations, and cross-functional resolution to drive design wins and product adoption while providing customer feedback to influence product roadmaps.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Lead technical strategy and architecture for CoreWeave's orchestration platform (SUNK). Own critical orchestration components, drive cross-org initiatives in scheduling, quota enforcement, and hyperscale scaling. Mentor senior engineers, establish reliability and observability best practices, and ensure workloads run reliably and efficiently for large GPU ML training and inference clusters.

Lead and scale teams building ServiceNow's AI evaluation platform. Define strategy and roadmap for benchmarks, automated scoring, verifier systems, and quality monitoring. Deliver unified internal and customer-facing evaluation services with APIs and dashboards. Drive novel evaluation methods, safety assessment, and cross-functional collaboration to ensure reliable, production-grade AI quality.

Lead a team building evaluation infrastructure and benchmarks for agentic and generative AI. Design automated tests, validate RAG pipelines, benchmark frontier LLMs, and translate evaluation results into product and risk recommendations to ensure reliable, safe, and cost-effective autonomous workflows.

Lead full-cycle recruiting for software engineering, infrastructure, platform, solutions engineering, technical leadership, and go-to-market roles. Build proactive talent pipelines, source passive candidates, advise hiring managers on market conditions, manage interview processes, and improve recruiting operations, ATS workflows, reporting, and candidate experience. Partner with executives, HR leadership, and agencies while establishing scalable recruiting practices for a rapidly growing SaaS startup.

Lead Strata from inception as co-founder, defining the vision for regulatory intelligence software, engaging enterprise customers, shaping the product roadmap, providing technical leadership for trusted AI agents, and recruiting teams across go-to-market, product, engineering, and customer success. The role requires early-stage B2B SaaS experience, hands-on LLM and agentic architecture expertise, and the ability to build products emphasizing accuracy, citations, auditability, and expert review.

The Staff Field Application Engineer drives adoption of Arm server SoCs, providing technical support, performance analysis, and training to customers while collaborating with internal teams on system integration and optimization.

Design and build automation tools, dashboards, and systems to deploy and manage cloud-based infrastructure. Collaborate with cross-functional teams to analyze requirements, implement reliable full-stack solutions (React front-end, Go back-end), improve CI/CD pipelines, and maintain production stability while meeting government background/clearance requirements.

Build and scale test automation and contract testing frameworks for AI-driven detection systems across cloud, agent, and browser extension surfaces. Expand unit, integration, contract, and end-to-end coverage, define AI efficacy testing, implement observability and quality telemetry, and collaborate with SRE and engineering to ensure reliable, high-performance SaaS deployments.

Design and build highly scalable cloud-native microservices and platform components for AI Detection and Response. Own architecture, testing, monitoring, and operational excellence; integrate with CrowdStrike services; troubleshoot distributed systems; mentor engineers and drive secure, reliable deployments at scale.

Own end-to-end quality for AIDR services across backend and UI: build reusable test frameworks, validate functional/performance/contract/chaos scenarios, increase automated coverage, mentor engineers, and improve observability and resilience for large-scale cloud systems.

Lead deployment, inference optimization, resource scheduling, profiling, and stability initiatives for AI models across autonomous vehicles and cloud infrastructure. Design low-latency inference pipelines, optimize GPU execution and memory usage, support LLM serving and model validation, diagnose software-hardware integration issues, and collaborate with perception, prediction, cloud infrastructure, and safety teams.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

This role focuses on driving adoption and alignment for ODM partners, supporting data center delivery, and incorporating partner feedback into product strategy.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

Architect and scale high-cardinality observability and telemetry infrastructure for AI cloud platforms. Build Kubernetes exporters, eBPF diagnostics, hardware and network monitoring, dashboards, alerting, and GPU/network utilization billing pipelines. Establish telemetry standards for AI workloads, support proactive remediation, collaborate with GPU and scheduling teams, lead architecture reviews, and mentor engineers. The role requires expertise in Go, Kubernetes, Prometheus/OpenTelemetry, Linux performance tuning, eBPF, AI hardware metrics, and large-scale cloud or HPC telemetry systems.