Top AI Engineer Jobs

Lead Uber’s security strategy and engineering for AI systems, including LLMs, agents, and AI-driven infrastructure. Design and ship secure, scalable systems; establish architecture for identity, access, monitoring, threat detection, data protection, and zero trust; investigate incidents; influence company-wide engineering practices; mentor senior engineers; and partner with security, infrastructure, product, and AI/ML leaders. The role requires 10+ years of software engineering experience and deep cybersecurity expertise.

Participate in a 12-week internship supporting database and data engineering initiatives. Design, maintain, optimize, and troubleshoot SQL stored procedures, databases, tables, indexes, and replication environments. Create SSIS packages for data transformation and transfer, perform data analysis and profiling, support production data loads, document data flows, and implement validation and unit testing to ensure data quality. Collaborate with technical and business teams on requirements, enhancements, bug fixes, and development standards.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Develop and optimize GPU-accelerated numerical linear algebra libraries for AI and high-performance computing. Design kernel generation, JIT compilation, matrix operations, and operator fusions; lead projects involving multiple engineers and teams; provide technical mentorship; collaborate with product management and customers on requirements and roadmaps; and improve performance and maintainability through architectural enhancements.

Design and develop a C++-based system for simplifying and accelerating computing in DL and HPC, alongside evaluating performance and improving library quality.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.

Develop and promote NVIDIA world models for autonomous vehicles through technical content, benchmarks, demos, workshops, developer engagement, and partner enablement. Design and post-train foundation models, prototype generative AI systems, explain technical concepts to business and developer audiences, and collaborate with research, software, product, sales, and customer teams. Serve as an autonomous-vehicle world-models expert while advancing thought leadership, adoption, and deployment of NVIDIA AI platforms.

Lead the hands-on architecture and development of NVIDIA’s enterprise agentic AI platform. Build production systems in Python or Go, including multi-agent orchestration, RAG, memory, Kubernetes deployment, GPU inference, evaluation pipelines, observability, security, sandboxing, and policy enforcement. Rapidly prototype emerging approaches and harden successful solutions into reusable APIs, SDKs, services, and developer templates while influencing cross-functional engineering and business teams.

Develop compiler optimization algorithms for deep learning networks and AI inference platforms. Collaborate with software framework and GPU architecture teams to improve performance, build times, memory usage, APIs, and compiler techniques for current and future NVIDIA GPUs. Analyze performance, implement and test compiler technologies, support CPU/GPU workloads, and independently lead development efforts.

Designs and scales observability and telemetry platforms for distributed AI and HPC environments. Builds high-performance backend services, OpenTelemetry components, metrics pipelines, and real-time or batch data systems. Improves reliability, performance, capacity, and cost efficiency while developing monitoring, alerting, and service reliability frameworks. Requires production distributed-systems expertise, programming skills, time-series data experience, Kubernetes knowledge, and operational debugging capabilities.

Develop production-grade integrations for NVIDIA Grove across AI frameworks including Dynamo, llm-d, Ray, and PyTorch. Build adapters, plugins, operators, runtime components, reference workflows, and developer tooling. Optimize distributed multi-node and multi-GPU training and inference, improve Kubernetes observability, define APIs, and troubleshoot containers, networking, scheduling, CUDA, and framework behavior. Collaborate with engineering teams and open-source communities to upstream maintainable changes and ensure compatibility across rapidly evolving dependencies.

Lead the design and optimization of GPU-cluster infrastructure supporting AI and ML research. Partner with researchers, data engineers, and DevOps teams to identify bottlenecks, improve researcher efficiency, and build scalable, highly available systems. Drive infrastructure roadmaps, optimize resource utilization, define performance metrics, and oversee distributed training operations across HPC environments. The role requires deep expertise in accelerated computing, storage, scheduling, networking, containers, cloud platforms, and AI/ML frameworks.

Principal-level software engineering leader responsible for architecting, building, and operating reliable enterprise AI applications and agent infrastructure. The role spans frontend, backend, distributed services, data flows, deployment, observability, testing, release engineering, security, and incident response. Responsibilities include developing reusable orchestration, memory, evaluation, telemetry, and policy capabilities; operationalizing NVIDIA AI technologies; defining secure agent collaboration architecture; mentoring engineers; and partnering cross-functionally to deliver production-grade AI systems.

As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.

Build NVIDIA’s AI-native TensorRT framework using agentic workflows and multi-agent orchestration. Architect scalable systems for AI-generated software, prototype state-of-the-art deep learning capabilities, and deliver high-performance inference for language, diffusion, audio, vision, and multimodal models. Optimize Python orchestration and C++ engine implementations for latency and throughput while translating customer needs into production-quality solutions.

The role involves engaging with AI-native companies to optimize their AI platforms using NVIDIA technologies, focusing on architecture, model performance, and collaboration with technical teams.

Build scalable tools and workflows for analyzing and optimizing AI training and inference performance. Integrate profiling infrastructure with AI frameworks, identify workload bottlenecks, automate diagnosis, and translate engineers’ needs into intuitive tooling. Collaborate with performance engineers, hardware architects, and software teams to align profiling capabilities with real-world workloads.

Build and ship AI coding agents, code LLMs, evaluations, datasets, MCP servers, and Agent Skills for CUDA developers. Fine-tune and evaluate models, generate synthetic code data, develop RAG and tool-use systems, and extend ComputeEval benchmarks. Collaborate with NVIDIA product teams to turn applied AI research prototypes into production features used by developers and customers.

Develop Pythonic CUTLASS DSL infrastructure and APIs for high-performance GPU programming. Build AST transformations, JIT compilation, debugging and profiling tools, validation systems, package distribution, documentation, and testing. Collaborate on open-source framework development while optimizing developer experience and GPU kernel delivery.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Profile, benchmark, and optimize large-scale LLM training and inference across GPUs, CPUs, HCAs, and switches. Focus on collective communication and high-performance networking, build PyTorch trace-based profiling and replay tools, define performance test plans, and collaborate across hardware and software teams to identify bottlenecks and achieve performance targets.

Build AI-driven compiler intelligence for production compiler pipelines. Design compiler optimization workflows, develop learning-based decision systems, integrate LLMs with compiler infrastructure, and validate performance across workloads. Collaborate with compiler, architecture, and performance teams to deploy reliable, scalable solutions that improve runtime, compile time, and efficiency.

Design and deliver high-performance, area- and power-efficient ASIC/GPU micro-architecture and RTL. Develop agentic AI solutions to craft micro-architecture, implement RTL, and produce fully verified, synthesis/timing-clean designs. Collaborate with designers and tool owners, and build LLM-powered engineering assistants, multi-agent systems, and multi-modal dialogue tools to accelerate chip design workflows.

Primary point-of-contact for a customer, providing on-site and remote HPC, Ethernet, and AI infrastructure support. Troubleshoot Linux systems, networking protocols, and interoperability; reproduce and resolve complex issues; collaborate with engineering, marketing, and support; document support methodologies and improve processes.