Artificial Intelligence Jobs in Santa Clara, CA

As a Software Development Engineer, you will create high-performance Linux software components, ensure system reliability, collaborate with teams, and design security features for containers in cloud environments.

Lead design, development, deployment, and support of large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization to improve scalability, cost, latency, and throughput while guiding technical vision and cross-functional delivery.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

Lead design, build, and operate scalable GenAI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Collaborate cross-functionally, invent LLM optimization techniques, and shape long-term technical vision for foundational AI systems.

Lead go-to-market strategy, positioning, and field enablement for AI Protect. Build differentiated messaging, competitive intelligence, persona-driven content, and sales enablement assets to accelerate deal wins and translate product roadmap into market impact.

Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.

Design, build, deploy, and support large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Collaborate cross-functionally, optimize model performance and cost, and contribute to technical vision and roadmap for foundational AI platforms.

Design and implement scalable, low-latency AI security infrastructure and high-performance networking code in Rust. Collaborate with product and cross-functional teams, debug complex network/system issues, and deliver secure, testable solutions for large-scale cloud security platform.

Design and build scalable microservices and platforms for AI security event processing, mentoring team members, and ensuring operational excellence.

The role involves designing, developing, and supporting AI software components, leveraging various AI technologies, and optimizing performance for large-scale AI systems.

The role involves architecting a scalable AI platform, building distributed compute solutions, optimizing technologies, and mentoring internal talent in AI and ML.

As a Senior Lead AI Engineer, you will develop AI solutions, partner with cross-functional teams, optimize LLM performance, and contribute to the AI vision at Capital One.

Lead design and delivery of production autonomous agents and agentic workflows: architect orchestration, integrate and optimize frontier LLMs, drive model fine-tuning and evaluation, mentor engineers, and translate business goals into safe, reliable, scalable AI-powered product experiences.

Design and build scalable AI platform capabilities to enable multi-step GenAI workflows, model execution, and integrations. Implement backend services, execution infrastructure, APIs, and observability to support production GenAI experiences across sales and product teams. Collaborate with AI engineers, data scientists, and product partners to turn AI use cases into reliable, extensible platform features.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

Serve as the field technical authority bridging enterprise AI and security: drive AI security strategy, lead demos and POVs, advise on LLM/RAG/agent risks and data protection, influence product roadmap, and create reusable enablement assets to scale sales and secure production AI deployments.

Lead and grow an AI engineering organization building foundation-model training and LLM-powered production systems. Own design, deployment, optimization, evaluation, governance, and observability of scalable AI platforms; make build-vs-buy decisions across open source and SaaS AI technologies; drive technical vision and mentor engineering teams to deliver responsible, high-performance AI capabilities.

Lead design, build, deploy, and support foundation model training and LLM inference systems, similarity search, guardrails, evaluation, and observability. Optimize production AI for scalability, cost, latency, and throughput. Collaborate cross-functionally, mentor engineers, and shape long-term foundational AI technical vision and roadmap.

Lead design, development, deployment, and support of large-scale AI systems including foundation model training, LLM inference, similarity search, guardrails, model evaluation, and observability. Collaborate cross-functionally to optimize performance, cost, and scalability, contribute to technical vision, and mentor engineering teams.

Develop and maintain large-scale evaluation, introspection, and visualization tools for autonomous vehicle ML models. Build metrics, dashboards, and pipelines that process billions of examples, collaborate with data, infra, simulation, and modeling teams, and provide technical leadership to align offline evaluation with real-world AV behavior.

Define memory and storage solutions for automotive systems, analyze system architectures, develop models for AI workloads, and engage with customers.

As a Lead AI Engineer, you will develop and support AI products, enhance system performance, and collaborate across teams to innovate AI solutions for Capital One.