Top AI Engineer Jobs

Designs, develops, and deploys AI-powered tools, agents, workflows, and enterprise applications. Partners with business and operations stakeholders to identify automation opportunities, translate requirements into scalable solutions, and integrate LLMs, APIs, cloud AI services, and enterprise technologies. Establishes prompt engineering, validation, governance, monitoring, and responsible AI practices while creating user interfaces and dashboards. Evaluates emerging technologies and contributes to AI architecture, strategy, and innovation initiatives.

Designs, develops, and deploys AI-powered applications, agents, workflows, and automation solutions that improve business operations. Partners with stakeholders to translate operational challenges into scalable AI systems, integrates LLMs and cloud AI services, establishes prompt engineering and governance practices, and creates user-friendly tools and dashboards. The role also evaluates emerging technologies and contributes to AI architecture, strategy, and innovation initiatives.

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.

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.

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.

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.

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.

Architects and delivers enterprise integration and API platforms using event-driven, asynchronous, cloud, and AI-augmented workflows. Develops reusable frameworks across SAP, SaaS, and partner ecosystems; governs APIs; integrates EDI, REST, GraphQL, and SOAP systems; and builds autonomous, self-monitoring and self-healing workflows. The role also requires programming, identity and access control expertise, CI/CD automation, observability, and close collaboration with Product and Engineering teams.

Engage leading academic AI labs researching foundation models, LLMs, multimodal systems, reasoning, training, inference, and scalable AI infrastructure. Advise researchers on NVIDIA platforms, identify impactful workloads, track frontier research, translate academic feedback into product and platform opportunities, and support technical workshops, university programs, and research conferences. The role requires deep foundational AI expertise, hands-on experience with AI training or inference, and strong research engagement credibility.

Develop AI-driven software for VLSI and circuit design automation. Research electronic design automation techniques, build agentic AI solutions, analyze datasets, validate hypotheses, and create machine learning models and algorithms to optimize circuit and layout design outcomes. Collaborate across engineering and research teams on pre-silicon and post-silicon design, circuit optimization, and SPICE correlation.

Develop AI-driven software systems for VLSI and circuit design, including machine learning models, combinatorial optimization algorithms, and agentic AI workflows. Analyze datasets, validate hypotheses, and build algorithms for circuit and layout optimization, pre- and post-silicon design, and SPICE correlation. Collaborate across engineering and research teams while advancing electronic design automation solutions.

Own Jetson’s developer experience, system performance, competitive intelligence, and product roadmap for edge AI and robotics. Lead requirements and release readiness across real-time execution, low-latency networking, RDMA, memory optimization, profiling tools, SDK usability, onboarding, documentation, and sample code. Partner with engineering, customers, partners, ISVs, and developers to prioritize performance-critical features and strengthen NVIDIA’s platform leadership.

Lead development and optimization of local AI agent frameworks and runtimes for Windows PCs with NVIDIA GeForce RTX GPUs. Build secure, sandboxed, privacy-focused agent execution and inference systems, collaborate with AI research, driver, and open-source teams, and establish deployment guidelines. Mentor engineers while delivering production-ready code using Windows systems programming, C++, Python, LLM inference pipelines, GPU acceleration, and agent orchestration frameworks.

Build the developer ecosystem for NVIDIA Dynamo, an open-source distributed AI inference framework. Responsibilities include creating demos, sample code, documentation, videos, technical talks, workshops, and office hours; collaborating with inference projects and open-source maintainers; troubleshooting deployments; gathering developer feedback; influencing engineering and product priorities; and communicating progress externally. The role requires hands-on experience with model serving, Kubernetes, distributed inference, performance optimization, and developer or partner programs.

Provide advanced technical support for NVIDIA Ethernet switching and AI infrastructure products. Troubleshoot Linux, server, hardware, operating system, networking, installation, interoperability, and operational issues for enterprise customers. Analyze protocols and packet captures, lead critical issue resolution, collaborate with Engineering, Marketing, and Support, and develop support methodologies and documentation. The role focuses on data center, HPC, networking, and AI infrastructure environments.

Lead software safety for AI-based autonomous vehicle systems across NVIDIA’s DRIVE and Halos platforms. Define and verify safety requirements, perform system and architectural safety analyses, develop safety plans and certification evidence, improve AI safety processes, and support interactions with customers, regulators, assessors, and standards organizations. The role covers safety-critical software, embedded platforms, autonomous systems, and certification activities.

Engage university researchers and laboratories to accelerate adoption of NVIDIA AI and computing platforms. Advise research teams, integrate NVIDIA frameworks into scientific workloads, identify ecosystem opportunities, monitor research trends, coordinate conferences, and translate researcher feedback into product roadmaps and enablement programs. The role requires deep AI and computational science expertise, research credibility, hands-on experience with modern AI systems, and strong relationships across academic and open-source research communities.

Design and implement GPU performance profiling tools focused on AI applications. Build features used by CUDA developers, collaborate with NVIDIA compiler and CUDA driver teams, and influence tool design. The role requires strong C++ software engineering experience, GPU hardware knowledge, and familiarity with performance tools, with preferred expertise in CUDA kernel programming, profiling, and DWARF parsing.

Builds secure AI and agentic cybersecurity tooling, transforming research prototypes into production-ready services. Responsibilities include developing backend services, APIs, CLI tools, automation, and integration patterns; defining MVPs for ambiguous security and identity challenges; collaborating across research, product, enterprise security, and platform teams; and applying offensive security and adversarial thinking to address AI-specific risks such as injection, tool misuse, unauthorized access, data exfiltration, and unsafe delegation.

Build agentic AI solutions and scalable infrastructure for NVIDIA GPU diagnostics, validation, debugging, and engineering workflows. Develop AI services, APIs, platforms, data pipelines, and integrations with engineering tools and knowledge bases. Establish evaluation, observability, security, and reliability practices for production AI systems. Partner across teams to identify use cases, define requirements, deploy solutions, and measure business impact.

Develop and integrate networking software for NVIDIA BlueField and ConnectX platforms supporting large-scale AI infrastructure. Work with customers and internal teams on RDMA and RoCE solutions, troubleshooting issues across applications, libraries, drivers, firmware, and switches. Analyze performance metrics, create automation and test cases, validate fixes, and support deployments from testing through production while documenting technical findings and decisions.

Develop and optimize AI media software for cloud and embedded NVIDIA platforms. Build low-latency inference systems, profile GPU workloads, improve performance and resource usage, and maintain CI/CD, packaging, and testing infrastructure. Collaborate with algorithm, platform, infrastructure, and QA teams to ship production SDKs and developer libraries, while driving projects from design through validated release.