Top Artificial Intelligence Jobs

Analyze, profile, and optimize AI training workloads on NVIDIA GPUs across the hardware/software stack. Implement production-quality software, build MLPerf training submissions, model workloads in simulators, and develop automation tools to identify and resolve performance bottlenecks.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Lead product marketing for AI infrastructure: analyze market and competitive signals, develop positioning and messaging, create content (blogs, webinars, tutorials), support product launches, and partner cross-functionally with engineering, sales, and GTM teams to showcase NVIDIA platform value.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Enable end-to-end agentic AI GPU deployments on NVIDIA RTX and DGX by profiling and optimizing performance, contributing to open-source LLM/GenAI projects, delivering trainings and sample code, collaborating with drivers/architecture teams, and mentoring junior engineers.

Lead developer-focused performance optimization for RTX and DGX SoC platforms: collaborate with partners and internal teams to optimize CPU, AI, and 3D-graphics workloads, profile and debug CPU/GPU performance, influence next-gen SoC design, and mentor junior engineers. Occasional travel for conferences and partner site visits.

Lead developer advocacy for AI and agentic security: advise security companies, accelerate NVIDIA stack adoption, guide partner integrations, map ecosystems, collaborate cross-functionally, run community events, and feed product teams with field insights.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal 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.

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Own global channel strategy and execution for NVIDIA Jetson and IGX platforms. Manage distributor relationships, drive partner onboarding, pipeline creation, revenue, and partner readiness. Collaborate with marketing on channel programs, MDF-funded activities, demand generation, and performance measurement through reviews, scorecards, and dashboards.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

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.

Lead technical and operational programs for enterprise AI partners. Define schedules, translate customer requirements, drive cross-functional execution from design to production, manage risks and post-deployment support, and coordinate global teams to integrate complex AI model stacks into customer environments.

Lead product strategy and roadmaps for inference tools, SDKs, and libraries that enable developers to deploy and optimize GenAI models on NVIDIA GPUs. Partner with internal and external developers, engineering, leadership, and marketing to prioritize features, drive product delivery, and define go-to-market plans focused on performance, cost, and safety.

Lead architecture and development of next-generation scale-up fabrics and collaborative communication for AI data-centers. Ensure scalability, reliability, and robust large-model training across hardware, software, and applications while driving innovation and cross-functional implementation toward production and manufacturing.

Lead the architecture, development, deployment, and governance of enterprise Generative and Agentic AI systems. Manage and mentor AI engineers, collaborate with business and product leaders, establish responsible AI and MLOps practices, and oversee scalable production solutions. Remain hands-on with machine learning, deep learning, LLMs, RAG, multi-agent systems, Python services, containerized deployments, monitoring, and optimization for cost, latency, and performance.

Leads two scrum teams while remaining hands-on in software development at least 50% of the time. Designs, codes, tests, debugs, and supports secure, resilient, high-throughput distributed payment systems using Java and Spring Boot. Drives AI-assisted engineering practices, automation, CI/CD, code quality, operational stability, and responsible AI usage. Evaluates technical solutions with vendors and internal teams and leads engineering communities of practice.

Build and own a shared AI and data platform for translational research. Responsibilities include provenance tracking, evaluation, access control, knowledge graphs, ontology engineering, governed data connections, common data models, containerized service deployment, CI/CD, monitoring, rollback, and usage measurement. The role develops production AI agents and tool servers for biomarker research, supports translational scientists, establishes engineering standards, and packages validated tools for long-term ownership by platform and research teams.

Lead BrightHire’s multi-product strategy across AI-powered hiring products, defining market direction, prioritizing investments, and translating strategy into high-quality execution. Partner with executive, engineering, sales, marketing, and customer success teams to drive adoption, retention, revenue, and growth through Zoom’s broader platform. Build AI-native products with strong reliability, trust, usability, and enterprise value while raising product judgment and execution standards across the organization.

Own the end-to-end go-to-market strategy for FinOps for AI, including messaging, customer proof, content, events, web, partner strategy, field enablement, and measurement. Coordinate across product, pricing, cost management, web, sales, and partner teams to create a consistent market story. Develop customer-facing and seller enablement materials, use customer signals to guide investment, and measure reach, engagement, consumption growth, and product adoption.

Develop and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and reliability. Own complex AI architecture, define reliability objectives, improve GPU/TPU utilization, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Leads quantitative and qualitative market research on international development, AI adoption, institutional demand, funding, procurement, competition, and commercial opportunities. Evaluates AI and data service applications, develops market assessments and strategic recommendations, manages research initiatives, prepares executive presentations, and collaborates with leadership, technical, operational, and business development teams to support market entry and growth.

Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.