Nvidia Manager System Software - Triton Inference Server 

United States, Texas 

43663430

What You Will be Doing:

• Guide, mentor, and develop an inclusive and collaborative engineering team focused on delivering robust model serving solutions.

• Drive planning, prioritization, and execution for projects that improve Triton’s scalability, performance, and reliability in non-generative AI deployments.

• Foster partnerships with Product and Program Management to create feature roadmaps, manage cross-team dependencies, and balance project resources for both cloud and on-premises platforms.

• Collaborate with internal collaborators and external customers to understand use cases and convert their needs into product features.

• Promote engineering excellence through modern, agile development practices and a culture of quality and accountability.

What We Need to See:

• Master’s or PhD, or equivalent experience, in Computer Science, Computer Engineering, or a related field.

• Eight or more years of overall hands-on software development experience in customer-facing environments.

• At least three years building, mentoring, and leading software engineering teams delivering production-grade solutions.

• Deep background in scalable serving architectures, with direct experience building cloud-native inference APIs,REST/gRPC/protobuf-basedservices, or similar technologies.

• Advanced C/C++ and Python development skills, demonstrating clean, object-oriented design, as well as proficiency in debugging, performance optimization, and testing.

• Track record of contributing to or leading large open-source projects—using GitHub for code reviews, bug tracking, and release management.

• Strong knowledge of agile methodologies and tools such as JIRA and Linear.

• Ability to communicate technical topics with clarity and empathy to colleagues, partners, and diverse audiences.

Ways to stand out from the crowd:

• Experience working within distributed, global teams.

• Practical knowledge of machine learning model deployment with frameworks such as TensorRT, TRT-LLM, PyTorch, ONNX, Python, or similar platforms.

• Understanding of CPU and GPU architectures.

• Skills in GPU programming (for example, CUDA or OpenCL).