Top Artificial Intelligence Jobs

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

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.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

The Solutions Architect will design and build enterprise AI solutions, aid in adopting NVIDIA AI technologies, and work on deep learning applications for customers across industries.

Leads Verizon’s network automation and infrastructure AI organization, driving autonomous, self-healing operations across virtualized RAN and 5G Core. Oversees cloud-native orchestration, closed-loop assurance, AI/ML optimization, energy management, policy-as-code, and on-premises AI inference at national scale. Builds distributed engineering, SRE, MLOps, and network automation teams; establishes autonomy standards and roadmap toward TM Forum Level 4. Partners with network intelligence teams, standards bodies, hyperscalers, and executive stakeholders while ensuring production security, resilience, and compliance.

Supports executives and operations by tracking coaching engagements, managing calendars and scheduling systems, drafting client communications, preparing program documents, administering collaboration platforms, and maintaining organized files. Develops and improves Claude- and Gemini-based AI workflows for document generation, transcript analysis, and scheduling. Handles ad hoc executive requests with discretion, prioritization, proactive judgment, and strong attention to detail.

Ensure Responsible AI principles are applied across AI lifecycle by documenting systems and use cases, maintaining model/use-case inventories and model cards, coordinating risk intake/tiering, compiling governance and audit evidence, supporting reviews and remediation, translating policy into operational controls, and partnering with Legal/Compliance to produce traceable, audit-ready governance reports.

Designs and deploys enterprise conversational AI applications using LLMs, agentic workflows, multi-agent systems, RAG, and prompt engineering. Builds evaluation frameworks, SDKs, APIs, developer tools, governance practices, guardrails, and monitoring capabilities. The role optimizes inference performance and scalability, evaluates emerging AI technologies, collaborates across technical and business teams, and mentors engineers while establishing production AI engineering standards.

Design, develop, and deploy enterprise-scale AI solutions, including LLM, NLP, computer vision, deep learning, multimodal, and RAG applications. Architect scalable training, inference, and MLOps pipelines; optimize models and infrastructure; establish evaluation, governance, security, and monitoring practices; collaborate across technical and business teams; mentor engineers; and guide AI architecture and technology strategy.

Optimize enterprise-scale AI training and inference systems for throughput, latency, scalability, reliability, and cost efficiency. Responsibilities include GPU and multi-GPU optimization, distributed training, LLM inference, profiling, model optimization, benchmarking, monitoring, cloud and FinOps optimization, and evaluating emerging AI hardware and frameworks. The role collaborates across AI, ML, platform, and infrastructure teams while providing technical leadership and mentoring.

Design, fine-tune, optimize, evaluate, and deploy enterprise-scale NLP and large language models. Build distributed GPU training pipelines, RAG systems, embedding and vector-search solutions, synthetic data workflows, model-serving infrastructure, and Responsible AI evaluation frameworks. Lead architecture reviews, establish LLM engineering practices, collaborate across research and product teams, assess emerging AI technologies, and mentor junior engineers while ensuring scalable, secure, compliant production deployments.

Designs, deploys, and maintains AI, machine learning, and generative AI solutions supporting sales forecasting, pipeline analytics, lead scoring, customer segmentation, and seller productivity. Builds predictive models, AI copilots, assistants, and RAG solutions; analyzes CRM and operational data; develops dashboards and executive reporting; and establishes MLOps monitoring. Partners with sales, revenue operations, and IT leaders to identify use cases, deliver scalable solutions, and drive data-informed commercial decisions.

Design, implement, and operate AI platform systems, developer tooling, and Azure integrations. Lead architecture, testing, deployment, and production operations for cloud-scale infrastructure (build/release, Kubernetes, networking, observability, automation, security). Collaborate across teams to define requirements, estimate work, and deliver resilient automation and tooling using Go, Python, and Terraform.

Leads complex enterprise AI, machine learning, generative AI, and agentic AI programs from planning through delivery. Coordinates engineering, product, architecture, platform, security, data, and business stakeholders; manages roadmaps, dependencies, risks, milestones, budgets, governance, and executive reporting. Supports responsible AI, lifecycle management, SDLC, Agile, security, and technology governance. Tracks technical deliverables involving APIs, cloud and data platforms, AI systems, and enterprise integrations using tools such as Azure DevOps, Jira, and Confluence.

Lead end-to-end product ownership for a suite of GTN AI/analytics products (Clarity, Compass, Dynamic Forecasting). Define vision, roadmap, and KPIs; prioritize use cases; drive delivery with cross-functional pods; manage vendors and platform selection; standardize and scale GTN workflows; ensure responsible AI, governance, and measurable business impact.

Owns enterprise architecture strategy for agentic AI infrastructure, including orchestration, identity, gateways, policy services, model hosting, and runtime environments. Defines roadmaps, standards, reference architectures, operational requirements, and production-readiness criteria. Leads complex cross-domain architecture decisions, builds prototypes, evaluates emerging technologies, guides engineering adoption, resolves risk and cost tradeoffs, and mentors architects and infrastructure engineers.

Own full enterprise sales cycles for a SaaS AI platform: run tailored demos, manage 2-week trials, engage multi-stakeholder buyers across R&D, regulatory, legal and procurement, partner with a solutions engineer, and help refine the sales playbook while closing complex deals at large manufacturers.

Designs enemies and NPC behaviors for a high-mobility multiplayer action game. Creates design specifications and technical documentation, collaborates with designers and engineers on scripting and behavior trees, analyzes player data and feedback to improve AI, coordinates dependencies with producers, and pitches new AI concepts to leadership.

Own Calendly’s user engagement strategy across in-product and email experiences. Build ranking, timing, and trigger systems that encourage users to return while protecting brand trust. Lead ML-powered personalization and experimentation informed by qualitative user insights, balancing company objectives with user needs. Ship quickly, learn from real usage, and drive sustainable long-term growth. This hybrid role requires working from the San Francisco office one to three times weekly and may involve occasional travel.

Build and maintain enterprise-grade Playwright automation frameworks, integrate automated testing into Azure DevOps CI/CD pipelines, and expand regression coverage across SaaS applications and APIs. Drive AI-assisted test generation using tools such as Claude, establish QA standards and metrics, prioritize automation opportunities, collaborate with engineering, product, and DevOps teams, and mentor junior QA staff.

Lead AI ecosystem go-to-market strategy by developing product marketing plans, positioning generative AI capabilities, facilitating partner workshops, creating technical and ROI-focused collateral, generating customer pipeline, and monitoring LLM, RAG, and AI agent trends. The role partners with product, sales engineering, and GTM teams to accelerate enterprise adoption through strategic AI ecosystem partnerships.

Architects and builds next-generation conversational AI products using generative AI. Leads and mentors engineering teams, establishes technical direction, partners with Product on scalable solutions, designs resilient cloud-native architectures, promotes AI tools and services, and drives quality through test automation. The role requires extensive full-stack development, enterprise SaaS architecture, production AI delivery, cloud platforms, and technical engineering leadership experience.

Design and evaluate AI-enhanced instructional materials and learning experiences, lead Agentic AI implementations, and integrate AI strategies into curriculum delivery. The role collaborates with educators and technologists, conducts needs analyses, evaluates learning effectiveness, delivers AI-in-education training, and supports continuous teaching and learning innovation.

Designs, develops, deploys, and maintains secure AI-integrated enterprise applications using C#/.NET, Python, Azure, LLM APIs, and modern front-end frameworks. Responsibilities include building APIs and interfaces, integrating and monitoring AI capabilities, managing cloud infrastructure and databases, implementing authentication and governance controls, evaluating prototypes, documenting systems, and supporting production handoffs. The role collaborates with managers, technical teams, attorneys, and legal professionals to deliver scalable AI tools in a regulated enterprise environment.

Build and own full-stack AI applications, data APIs, dashboards, and cloud-native services. Develop Python and Node.js backends, React frontends, AI/ML integrations, RAG pipelines, authentication, API management, testing, CI/CD, and performance improvements. Collaborate with AI/ML engineers, data scientists, product, and design teams to deliver scalable, reliable, intuitive, and responsible AI experiences.