NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. Its a unique legacy of innovation thats fueled by great technology—and amazing people. Today, were tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing whats never been done before takes vision, innovation, and the worlds best talent. As an NVIDIAN, youll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.We are seeking a Software Engineering Manager to lead our Inference Microservices Factory Engineering team. This team is at the core of NVIDIAs AI inference strategy, building and maintaining our distributed computing infrastructure that creates NIMs (NVIDIA Inference Microservices). You will be instrumental in defining the future of our factory automation and infrastructure across both cloud and on-premise environments, ensuring scalability, reliability, and efficiency for mission-critical inference workloads.What youll be doing:<ul><li>Lead and manage a high-performing team of software engineers and SRE engineers, guiding their professional growth and project execution while fostering a culture of innovation and excellence.</li><li>Oversee factory automation initiatives that streamline the development, deployment, and management of inference microservices across distributed environments.</li><li>Coordinate the development of infrastructure that ensures consistency, quality, and security for inference workload deployments at scale.</li><li>Collaborate with cross-functional teams to integrate the infrastructure into CI/CD pipelines, enabling seamless and efficient microservices delivery.</li><li>Build foundational distributed computing systems supporting the full lifecycle of inference microservices for NVIDIAs AI strategy.</li><li>Establish and enforce standards for infrastructure and application deployment, eliminating manual, ad-hoc processes through automation.</li><li>Work closely with security teams to ensure the platforms design and implementation are robust and secure, with a focus on authentication, authorization, and data protection.</li><li>Drive recruitment and mentorship efforts to build and maintain a top-tier engineering team.</li></ul>What we need to see:<ul><li>BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience).</li><li>8+ overall years of software engineering experience with a focus on distributed systems, cloud infrastructure, or large-scale platform development.</li><li>3+ years of experience leading and managing high-performing engineering teams of software engineers and SRE engineers.</li><li>Proven expertise with distributed computing technologies including Kubernetes, container orchestration, and experience with multi-cloud or hybrid-cloud environments.</li><li>Strong understanding of microservices architecture** and experience building scalable, fault-tolerant distributed systems.</li><li>Experience with factory automation and infrastructure-as-code principles, including Temporal and automated deployment pipelines.</li><li>Excellent communication, leadership, and problem-solving skills with the ability to operate in a fast-paced, collaborative environment.</li><li>Proven ability to work effectively in remote and cross-functional teams.</li></ul>Ways to stand out from the crowd:<ul><li>Experience building platforms that support the full lifecycle of AI inference applications and microservices.</li><li>Deep understanding of inference workloads and their unique infrastructure requirements for low-latency, high-throughput processing.</li><li>Experience with NVIDIA hardware including GPUs, DPUs, and networking technologies for AI workloads.</li><li>Background in AI/ML infrastructure and understanding of model serving, inference optimization, and GPU utilization.</li><li>Experience in a large-scale, high-growth technology company with proven track record of delivering software products.</li></ul>