Artificial Intelligence Jobs in Santa Clara, CA

Build and deploy production features for Marvell’s agentic AI software development lifecycle platform. Responsibilities include developing MCP integrations with engineering tools, automating CI/CD workflows, deploying containerized services on Kubernetes and AWS, writing production-quality code, measuring business impact, and collaborating with engineering and IT partners. The intern will work with LLMs, AI-assisted development tools, real users, and senior engineers.

Build and operate production-quality AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling systems. Optimize large language model training and inference across GPUs and Microsoft hardware, diagnose cross-stack reliability and performance issues, and improve model onboarding, hardware utilization, Azure efficiency, and deployment speed. Senior engineers own major components and projects; Principal engineers define technical direction, architecture, and multi-release strategy while leading cross-team initiatives and mentoring technical leaders.

Own the architecture and technical strategy for Square’s go-to-market technology stack. Build and operate production AI agents, automated workflows, and integrations that improve sales productivity. Lead complex technical initiatives, establish AI reliability and security guardrails, define evaluation metrics, and guide build-versus-buy decisions. Partner with Sales, Product, Data, and Engineering leaders while mentoring engineers and remaining hands-on across design, implementation, testing, deployment, and operations.

Lead AI product strategy and strategic operations across an enterprise software portfolio. Track revenue, customers, costs, metrics, and industry trends; support quarterly reviews and annual planning; conduct market sizing and buy-build-partner assessments; manage executive and cross-functional stakeholder alignment; and oversee AI initiatives that drive growth and operational efficiency.

Designs and leads production-grade test automation for complex digital and mixed-signal engineering and production environments. Builds CI/CD pipelines, hardware and instrument integrations, AI-assisted workflows, validation gates, observability, diagnostics, recovery processes, and reproducible containerized execution environments. Establishes reliable release criteria and mentors engineers while ensuring automation produces traceable, evidence-based results.

Designs cache subsystems for Intel AI SoCs, including microarchitecture, RTL coding, integration, simulation, timing constraints, and verification. Evaluates power, performance, and area tradeoffs; supports synthesis, timing closure, formal equivalence, silicon bring-up, post-silicon validation, and debugging. Requires advanced Verilog/SystemVerilog expertise, SoC integration experience, bus protocol knowledge, scripting skills, and leadership in mentoring engineers and improving design methodologies.

Build and deploy production-ready AI applications, agents, and workflow automations. Partner with attorneys and business teams to identify use cases, translate requirements, integrate enterprise systems, and deliver scalable solutions. Lead projects from prototyping through deployment, evaluate AI models and frameworks, establish engineering best practices, monitor performance, and improve reliability and user experience. Collaborate across Innovation, IT, Knowledge Management, and Business Operations while advising stakeholders on practical AI implementation.

Lead development of AI enablement roadmap, build state-of-the-art AI systems, and produce tools, services, and blueprints to accelerate AI adoption across NVIDIA. Collaborate with cross-organizational leaders and external partners to optimize productivity for software, hardware, and other workflows.

Lead end-to-end performance optimization for LLM and VLM inference on NVIDIA GPU systems. Analyze workloads, build performance models, profile systems, optimize CUDA kernels and serving runtimes, and tune batching, caching, quantization, speculative decoding, and parallelism. Establish reproducible benchmarks and regression gates while collaborating across model, framework, networking, and GPU architecture teams. Contribute improvements to open-source inference projects including TensorRT-LLM, vLLM, and SGLang.

Leads accounting systems transformation and finance automation, including NetSuite and Salesforce optimization, AI agent deployment, integrations, reporting, and internal tooling. Owns project plans, testing, training, go-live, controls, and executive reporting while partnering with engineering and cross-functional teams. Identifies automation opportunities, pilots AI vendors, supports SOX requirements, and trains finance staff on new workflows.

Define AppZen’s B2B payments product strategy, select and manage payment infrastructure partners, and own payment integrations from architecture through production. Design payment execution, monitoring, reconciliation, fraud prevention, and AI automation within enterprise controls. Translate complex payment requirements into scalable capabilities while collaborating with engineering, legal, compliance, sales, customer success, marketing, and external providers. The role requires hands-on experience with payment products, processors, banks, APIs, and enterprise payment lifecycles.

Builds and manages technology and industry partnerships from sourcing through integration, deployment, and expansion. Evaluates partner capabilities, strategic fit, customer demand, integration complexity, and business value. Coordinates engineering, product, commercial, and customer stakeholders to develop integrated solutions, accelerate deployments, and create new business opportunities. Serves as the primary owner of strategic partnerships, ensuring alignment on objectives, timelines, responsibilities, and outcomes.

Build and lead the Site Reliability Engineering function across cloud, colocation, on-premises labs, and customer-facing services. Establish SLOs, observability, incident management, IaC automation, self-healing infrastructure, FinOps, capacity planning, and shared-storage architecture. Lead SRE and data center teams, hire and develop engineers, manage reliability and technical direction, and serve as the senior incident escalation point.

Leads complex, cross-functional technology programs across AI, software, robotics, vehicle hardware, infrastructure, simulation, data, and product. Owns project delivery, improves efficiency, manages risks and dependencies, aligns stakeholders, supports business goals, and drives continuous process improvement. The role requires strong technical understanding, systems thinking, communication, project management, and influence without authority across engineering teams.

Lead company-wide AI transformation by architecting distributed AI platforms for data processing, model training, inference, and evaluation. Embed with autonomous driving engineering teams to remove bottlenecks and deliver AI DevOps and copilot ecosystems. Design LLMOps/MLOps automation, optimize GPU clusters and inference performance, guide technical roadmaps, and drive adoption of AI-native engineering practices across infrastructure, algorithms, and hardware teams.

Build and deploy AI agents, automations, and tools that transform organizational processes. Embed with teams to map workflows, deliver time-boxed solutions using Claude and related platforms, measure business value, document and hand off production-ready systems, troubleshoot during stabilization, and create reusable patterns. Partner with AI Platform Operations, IT, risk, and staff while ensuring responsible handling of sensitive data and teaching teams to maintain solutions.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Manages multiple AI research compliance programs, coordinating engineering, research, privacy, and review stakeholders. Responsibilities include drafting requirements, tracking milestones, resolving blockers, reporting project status, mitigating risks, cataloging and annotating AI research assets, analyzing data, and optimizing workflows. The role also contributes to internal consulting practice activities and requires strong communication, stakeholder management, and independent problem-solving in ambiguous environments.

Leads AI engineering strategy across clinical, patient, and operational products. Designs and operates production retrieval, orchestration, agentic, and evaluation systems, establishing standards for AI safety, observability, responsible deployment, and reliability. Partners with clinical, product, and executive leaders on the AI roadmap, mentors engineers, evaluates emerging models and tools, and codes regularly in Python.

Own growth marketing for an AI systems integrator by translating engineering work into compelling stories, videos, demos, and written content. Manage and measure channels including LinkedIn, YouTube, and X; run experiments against pipeline outcomes; and build lightweight tools, dashboards, scrapers, and microsites. The role requires strong writing, genuine AI interest, working-level Python or TypeScript skills, and comfort using AI coding tools. Candidates may be early-career and must be in New York or San Francisco or willing to relocate.

Own the end-to-end product management cycle, including customer discovery, user interviews, solution definition, product requirements, AI prototyping, engineering collaboration, feature launches, and communicating product value. The role focuses on rapidly delivering mobile and web products that improve construction workflows, using data and decisive ownership to address complex customer and operational needs.

Design, implement, and evaluate machine learning and deep learning algorithms for autonomy, trajectory prediction, and perception. Develop predictive and decision-making models, conduct experiments in simulated or dynamic environments, and build scalable research tools. Collaborate on research prototypes involving agent behavior, environmental understanding, and generative AI. Communicate findings to leadership and help transition validated research systems into production.

Conduct AI research for surgical robotics using multimodal surgical data, Visual-Language-Action models, computer vision, language models, and robot learning. Develop algorithms for surgical scene understanding, procedure recognition, and imitation learning; process large datasets; validate models in simulation and on robotic systems; collaborate with engineers and clinicians; and document research for technical reports, patents, or conferences.

Design, implement, and evaluate novel machine learning and deep learning algorithms, develop scalable research tools, benchmark emerging techniques, and transition research prototypes into production AI systems. The role requires collaboration with research engineers, experimentation across data, architectures, training, evaluation, and deployment, and communication of findings to leadership. Focus areas include transformers, multimodal foundation models, diffusion policies, fine-tuning, distillation, mixture-of-experts, and potentially reinforcement, self-supervised, imitation learning, and robotic VLA models.

Define next-generation data center GPU products by analyzing workloads, especially large language model training and inference. Identify customer opportunities, develop GPU value propositions and product proposals, and collaborate with engineering, operations, field teams, cloud partners, and sales on go-to-market plans, positioning, collateral, and tools. The role requires technical knowledge of GPU, software, and computing architectures, along with 7+ years of technology experience.