Artificial Intelligence Jobs in Sunnyvale, CA

As a Senior AI Engineer, you'll develop and deploy AI agents, focusing on model fine-tuning, infrastructure, and user interaction across social platforms, ensuring reliability and engagement.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

Design, build, and scale Databricks AI Runtime for large-scale GPU training. Lead architecture and implementation of multi-node orchestration, scheduling, checkpointing, resilience, throughput optimization, observability, and developer tooling. Mentor engineers, partner with product and research, and deliver high-impact production rollouts supporting thousands of accelerators and diverse model workloads.

As a Product Manager at Glean, you will evaluate LLM models, define the product roadmap, manage relationships with providers, and drive customer adoption of AI capabilities.

Drive architecture and implementation of Databricks AI Runtime for large-scale GPU training. Build scheduling, distributed training performance, resilience, observability, and developer-facing APIs/CLI. Improve GPU utilization, fault tolerance, and multi-node orchestration. Lead cross-team initiatives, mentor engineers, and deliver high-impact platform features supporting thousands of accelerators and long-running jobs.

Lead hardware-software co-design to enable AI/HPC and agentic AI stacks on RISC-V CPUs. Integrate and optimize low-level kernel infrastructure, runtimes, distributed execution, memory systems, and developer tools, collaborating with architects, compiler engineers, and kernel developers to meet performance targets.

Leads and grows a software engineering team developing cloud software, data platforms, and AI-agentic products. Sets technical direction, guides architecture and design decisions, owns delivery roadmaps, partners cross-functionally, and maintains technical depth for hiring and incident review. Establishes AI-assisted development practices and a strong production reliability and on-call culture while mentoring engineers and supporting career development.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Lead development of local AI agent frameworks and runtimes on Windows and GeForce RTX GPUs. Optimize agent inference, sandboxing, and privacy/security policies, collaborate with AI research and driver teams, mentor engineers, and deliver production-ready, local autonomous agent systems.

Own the strategy, roadmap, backlog, prioritization, and release planning for an enterprise Generative AI platform. Translate business requirements into scalable platform capabilities and engineering deliverables, while balancing value, usability, security, governance, and technical feasibility. Partner with AI Engineering, Data Science, Security, Privacy, Legal, Infrastructure, and business teams to drive adoption, reusable services, responsible AI practices, and measurable platform outcomes.

Develop and deploy LLM workflows and production machine learning models for nuclear-system design and analysis. Build surrogate models for real-time digital twins, manage data pipelines, maintain rigorous information controls, and lead AI initiatives from inception through delivery. The role also involves growing a software engineering team, presenting roadmaps and demos to leadership, gathering user feedback, and collaborating with nuclear engineers in a fast-paced startup environment.

Own Tenstorrent’s cloud sales segment from customer discovery through production deployment and revenue growth. Build pipeline, account strategies, forecasts, and a repeatable go-to-market motion for neoclouds, inference providers, model-as-a-service platforms, edge networks, and capacity aggregators. Identify differentiated AI workloads, lead technical evaluations and commercial negotiations, and partner with Product, Engineering, Field Applications, Marketing, and sales teams to drive adoption and strategic customer outcomes.

Leads supply chain data science and AI operationalization for Global Service Operations. Converts manual analytics into governed, automated AI-assisted workflows, taking solutions from prototypes to production with lineage, monitoring, lifecycle management, and ownership. Manages analysts, delivers against SLAs, develops statistical and machine-learning models, and communicates actionable insights across procurement, fulfillment, reverse logistics, inventory, and supplier operations. Measures cycle time, quality, and business impact while mentoring the team and advancing enterprise AI governance.

Lead the engineering team for Orchestrator, an AI-powered healthcare referral coordination product. Own end-to-end delivery, architecture, EHR integration strategy, scalability, customer partnerships, hiring, and team development. Remain hands-on through design reviews, code reviews, and production incidents while building generalized solutions across healthcare customers. Required expertise includes EHR integrations, HL7 v2, FHIR, full-stack production engineering, and at least one year of engineering management experience.

Build components of an AI Account Manager to automate hosting and optimization for advertiser accounts. Develop diagnosis and recommendation models using LLMs and delivery history, implement observability, dry-run safeguards, and evaluation pipelines, and collaborate with senior engineers and product to ensure reliable, auditable optimization that preserves account health and drives revenue.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

Lead architecture and integration of RISC-V-based AI compute clusters and vector engines. Define ISA extensions, memory subsystems, interconnects, task-mapping and scheduling, and performance/debug features. Collaborate with compiler, ML runtime, RTL, verification, and firmware teams to optimize performance-per-watt and scalability across silicon generations.

Verify chip-level and performance behavior of custom ASICs: design end-to-end test flows with architecture team, run simulation-based verification, implement UVM environments, use constrained-random and functional-coverage methodologies, and coordinate vendors/contractors.

Lead 0→1 product development for Emerald Cloud Services: define vision, conduct customer discovery, set product requirements for AI compute provisioning and management, partner with engineering and GTM teams, and launch and scale cloud services for enterprise and AI customers.

The CX Manager will own the ERP integration domain, providing problem-solving expertise for complex customer issues and ensuring efficient issue resolution related to ERP systems and AI operations.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

As an AI Product Engineer, you will develop and deploy AI-driven software solutions, collaborate with customers, and optimize applications for real-world impacts.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

Embed with customers to learn their workflows, redesign and build production GenAI/LLM systems on AWS, ship and operate code, evaluate non-deterministic models, and own business outcomes.

The role involves synthesizing, placing, and routing high-speed designs, collaborating with RTL teams, and optimizing designs for power/performance area.