Machine Learning Engineer

NVIDIA is looking for a talented Machine Learning Engineer to drive the development, evaluation, deployment and end-to-end lifecycle management of our AI-powered systems. This role bridges advanced AI application development with robust software engineering and continuous automation. You will extensively apply AI agents and build automated testing frameworks. You will also implement secure continuous integration and deployment pipelines with GitLab. These actions ensure code quality and system resilience. A core component of this role involves deploying and scaling models efficiently across distributed infrastructure. You will manage GPU orchestration, prompt-tune models, and build advanced AI workflows using platforms such as Kubernetes, Ray, or Slurm.

Architect, deploy, and scale open-source models using distributed orchestration frameworks. Examples include container orchestration platforms like Kubernetes, distributed computing frameworks such as Ray, or workload managers like Slurm. These frameworks support highly available and fault-tolerant AI workloads. AI Systems & Data Pipelines: Design and build machine learning systems and data pipelines. Design experiments, prompt-tune, evaluate, and deploy production-grade models and AI agents, implementing flexible mechanisms to benchmark performance and swap models quickly to fit evolving use cases. Error & Gap Analysis: Run comprehensive model benchmarks, perform deep error and gap analysis on model outputs, and build analytics dashboards to communicate system performance findings effectively to stakeholders. Independent Execution: Take high ownership of features from ideation to production, managing architectural choices, coordinating updates across both accessible and restricted code repositories, and community interactions. What we need to see: You have a Master’s or PhD in Computer Science, Electrical Engineering, or a related field - or equivalent experience. Python & Systems Engineering: 3+ years of professional experience writing production-grade, asynchronous Python, with a strong focus on decoupled, clean system architecture and design patterns. AI tools & ML Frameworks: Deep experience building with LangChain, Hugging Face libraries, vLLM, and SGLang. Experience with ML frameworks like TensorFlow, PyTorch and Scikit-learn Data analysis: Proficient in data analysis using Python (pandas, NumPy, or similar), able to extract insights from model evaluation results and communicate findings clearly to both technical and non-technical collaborators. Deployment & Orchestration: Hands-on experience with production-grade model deployment, performance monitoring and analysis;  and scaling using Kubernetes, Ray, or Slurm to manage multi-node cluster configurations. Hardware & Scaling Optimization: Strong understanding of GPU memory management, and infrastructure-level tuning for high-throughput, low-latency AI inference workflows. GitLab CI/CD & Security Automation: Advanced knowledge of GitLab pipelines, specifically building automated test jobs and integrating vulnerability scanners directly into the MR workflow. Testing Toolchains: Expert familiarity with Python testing frameworks (e.g., PyTest), mocking libraries, and automated test generation frameworks for AI workloads. Advanced Version Control: High proficiency in advanced Git workflows, including rebase strategies, cryptographic commit signing, and managing complex public/private repository mirroring. Ways to stand out from the crowd: Experience with alignment/fine-tuning of LLMs, including regular LLMs as well as VLMs  (Vision-Language Models) or any-to-text Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research, and publication experience. With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the