Top Artificial Intelligence Jobs

Build and maintain data pipelines, ML workflow components, and platform features supporting production AI/ML solutions. Write tested, maintainable Python and SQL code, troubleshoot assigned pipelines, participate in design and scoping, and collaborate with senior engineers and cross-functional partners. The role is intended for an early-career engineer developing proficiency in cloud platforms, machine learning workflows, version control, testing, and MLOps practices.

Supports and enhances ATIS/ATMS systems for a 24/7 transportation management center. Develops Python and Java applications, web tools, APIs, dashboards, reports, database solutions, automation, and integrations. Maintains applications across Windows and Linux environments, troubleshoots operational issues, manages deployments and documentation, and supports system improvements involving analytics, AI, GIS, and ITS technologies. Provides technical guidance, training, and coordination with transportation personnel, vendors, and engineering teams.

Serve as strategic commercial and technology legal advisor, drafting and negotiating software, SaaS, licensing, data, customer, and vendor agreements. Advise on AI products, governance, responsible AI, privacy, intellectual property, regulatory compliance, product launches, and legal risk mitigation. Partner with Sales, Product, Engineering, and executive teams while supporting international and broader legal initiatives. Use legal technology and AI tools to improve legal service delivery.

Principal Software Engineer leading architecture and technical strategy for an in-vehicle AI assistant platform. The role spans Android Automotive client applications, cloud services, edge-cloud orchestration, conversational AI, gRPC, streaming interfaces, testing, telemetry, and distributed systems. Responsibilities include cross-functional architectural alignment, roadmap development, resolving complex reliability and privacy trade-offs, integrating AI frameworks, and mentoring senior engineering teams.

Own strategic product initiatives across Calico, Lynx, and emerging AI-focused products. Define product vision and roadmaps, competitive positioning, go-to-market strategy, customer discovery, messaging, and cross-functional execution from MVP through general availability and revenue. Partner with engineering, sales, marketing, customers, and ecosystem partners to deliver enterprise infrastructure, Kubernetes networking, security, and AI capabilities.

Design and scale Kubernetes-native, GPU-accelerated cloud architectures for large-scale robotics simulation, synthetic data pipelines, training, and inference. Advise partners, optimize scheduling, storage, networking, and GPU utilization, and drive adoption of NVIDIA Physical AI frameworks while collaborating with product and engineering teams.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Design and oversee datacenter-scale AI infrastructure architectures across networking, compute, storage, and Kubernetes ecosystems. Lead prototyping, testing, performance optimization, and lifecycle refinement for scalable AI products. Collaborate with hardware, software, security teams, customers, and partners to develop proofs of concept and MVPs. Produce technical documentation, user guides, whitepapers, blogs, and curricula while translating complex systems concepts for diverse audiences.

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.

Drive the development and roadmap of HPC and AI/ML products for physics, plasma, high-energy-density simulations, and resilient materials discovery. Define features, requirements, positioning, and go-to-market strategy; collaborate with engineering and NVIDIA software teams; identify customers and partnerships; and represent products at workshops and conferences.

Own the inference performance product roadmap across the full stack, turning advanced LLM optimization techniques into widely adoptable platforms. Define strategies for agentic and multi-turn workloads, partner with open-source and internal teams to land optimizations, and own benchmarking, release readiness, regression tracking, and production feedback loops to improve latency, efficiency, and cost per token.

Design and deploy scalable AI inference architectures for strategic partners. Build and operate GPU inference recipes, optimize pipelines using leading inference backends, and guide disaggregated serving deployments. Evangelize Kubernetes, compute fabric, DevOps, and observability practices while mentoring customers and internal teams. Lead technical engagements, resolve complex infrastructure issues, create reference architectures, and provide product insights. The role also involves agent design, sandboxing, memory and retrieval systems, evaluation, skill governance, and model customization.

Develop HPC and AI software workflows for experimental data processing, imaging, plasma, high-energy physics, and materials simulations. Profile applications, benchmark performance, identify bottlenecks, and advise collaborators and product teams. Build tutorial examples, translate research into software tools, and represent NVIDIA at scientific conferences and industry events.

Build and operate AI infrastructure systems, including telemetry pipelines, observability dashboards, incident-management automation, hardware and software catalogs, service ownership data models, and self-service discovery tools. Lead cross-functional infrastructure initiatives and develop AI-assisted operational tooling. The role requires production software and infrastructure expertise, programming experience, and the ability to improve reliability, visibility, incident response, and operational efficiency across NVIDIA GPU cloud environments.

Designs, deploys, maintains, and tunes large-scale HPC and AI clusters. Responsibilities include workload scheduling, infrastructure automation, CI/CD pipelines, monitoring, logging, alerting, storage and networking deployment, bottom-up troubleshooting, methodology documentation, and R&D support. The engineer works across hardware, operating systems, GPU computing, virtualization, cloud platforms, and software stacks to deliver performant, reliable self-service infrastructure.

Lead product strategy and multi-quarter roadmaps for NVIDIA’s AI Factory software-defined networking stack. Guide upstream development across OVN, OVS, and OVN-Kubernetes; define requirements, priorities, and architecture trade-offs; coordinate dependencies across DPU, fabric, virtualization, and engineering teams; and translate partner and operator needs into product releases, reference deployments, and community strategy.

Builds developer and customer relationships across the Jetson ecosystem, guides partners through embedded Linux and Jetson software integration, promotes NVIDIA AI libraries and edge AI capabilities, supports production deployments, and communicates developer feedback to engineering and product teams to shape platform roadmaps, reference designs, positioning, and adoption.

Leads the design, development, and deployment of scalable, reliable software and infrastructure solutions. Builds AI agents, automation workflows, observability integrations, and monitoring systems using technologies including C++, Python, Java, Claude, Codex, BigPanda, ITMP, and Prometheus. Collaborates across teams, drives engineering standards, mentors junior engineers, and manages complex end-to-end projects.

Design and develop next-generation scale-up networking fabrics for AI data centers. Lead collaborative development across hardware, software, and applications to improve scalability, reliability, and robustness for large-model training. Create innovative concepts through production implementation, collaborate with cross-functional teams, and lead complex projects delivering large-scale networking and AI solutions.

Designs and deploys large-scale GPU infrastructure for public-sector customers. Leads solution architecture across compute, storage, networking, cluster deployment, integration, performance optimization, and troubleshooting. Acts as a customer technical advisor, coordinates engineering and product teams, delivers technical presentations and workshops, and supports customer deployments at data centers. Requires expertise in accelerated computing, AI infrastructure, high-performance networking, schedulers, benchmarking, and Kubernetes.

Owns positioning, messaging, campaigns, and go-to-market coordination for NVIDIA’s Local AI platform. Translates technical capabilities, developer workflows, performance data, and customer outcomes into compelling stories and marketing assets for developers, enterprises, OEMs, partners, and AI enthusiasts. The role requires hands-on local AI building experience, strong generative AI and GPU knowledge, technical fluency, market analysis, community engagement, and cross-functional leadership.

Lead regional enterprise sales for NVIDIA’s Physical AI and digital twin solutions. Build executive customer and partner relationships, develop end-to-end solution sales plays, enable global sales teams, manage lighthouse opportunities, track market adoption, and provide customer insights to product teams. The role focuses on industrial manufacturing, factory planning, robotics, synthetic data, intelligent infrastructure, and expansion across APAC and the Americas.

Manage NVIDIA’s global Lenovo OEM account and drive adoption and integration of NVIDIA accelerated computing platforms. Develop technical expertise across AI hardware, networking, software, and reference architectures; identify solution opportunities; package NVIDIA technologies into Lenovo offerings; and develop co-marketing and co-selling strategies. Coordinate product, engineering, supply planning, pricing, quality, and go-to-market teams while building relationships with senior stakeholders and supporting cloud and enterprise sales channels.

Develop production-grade multimodal AI systems for L3/L4 autonomous vehicles. Build sensor-fusion and spatiotemporal world models combining camera, LiDAR, radar, navigation, and vehicle-state data. Create multitask models for road understanding, occupancy, perception, prediction, and planning; develop scalable training and evaluation pipelines; investigate foundation-model approaches; optimize models for real-time NVIDIA hardware; and collaborate across AV engineering teams to deploy robust systems.

Develop production AI-powered systems that improve software quality, engineering productivity, debugging, and developer workflows. Build intelligent services involving code analysis, retrieval, orchestration, evaluation, monitoring, and large-scale engineering data. Partner across teams to translate workflow challenges into reliable AI solutions, evaluate models and frameworks, and optimize quality, latency, cost, and reliability from concept through deployment and continuous improvement.