Senior MLOps Engineer – DSX Enablement

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Create cutting-edge solutions to enhance AI infrastructure capabilities.

• Provide guidance to infrastructure specialists regarding the requirements of ML workloads.

• Assist practitioners in diagnosing and resolving full-stack AI and ML system issues.

• Support both internal and external clients’ AI and ML projects, which include evaluating LLM performance and integrating new hardware into open-source frameworks.

• Design and implement tailored AI solutions on NeoCloud platforms and with NVIDIA Cloud Partners, covering distributed training, inference optimization, and MLOps pipelines.

• Serve as the main technical liaison for internal and external clients and partners.

• Oversee collaborative efforts, ensure successful initiatives on DGX Cloud, and address complex production challenges.

• Work alongside infrastructure software and accelerated-framework teams.

• Analyze and optimize large-scale training and inference workloads on NVIDIA Cloud Partner platforms.

• Lead initiatives to minimize latency, costs, and operational risks.

• Create open-source tools and reference architectures for machine learning and AI workloads, pipelines, and systems at scale.

⛳️ Requirements

• Bachelor's, Master's, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical discipline, or equivalent experience.

• Over 8 years of experience in technical roles such as data science, data engineering, or ML engineering, ideally focused on large-scale production systems.

• Proven AI/ML experience through various stages of the machine learning lifecycle, from exploratory analysis to deployment.

• Proficient in Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced networking at datacenter scale.

• Strong scripting and programming abilities in Bash and Python.

• Solid systems programming expertise in C++, Go, or Rust.

• Experience with machine learning or deep learning frameworks for both training and inference.

• Exceptional communication and technical presentation skills, capable of explaining architectures, trade-offs, and recommendations to engineering and leadership audiences.

• A demonstrable record of engineering discipline and successful execution on engaging projects.

• Experience in contributing to and collaborating within open-source communities.

• Familiarity with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, InfiniBand, NVLink, and RoCE.

• Experience in developing machine learning systems within security-critical environments and utilizing distributed training and inference frameworks.

• Understanding of MLOps practices in a cloud-native environment, encompassing containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps workflows.

• Direct experience in diagnosing and resolving performance or correctness issues across hardware, networking, accelerators, hypervisors, operating systems, compilers, runtimes, application code, and libraries.

🏝️ Benefits

• Competitive salaries.

• Generous benefits package.

• Equity.