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.
What you'll be doing:
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 most forward-thinking and hardworking people in the world working with us and our engineering teams are growing fast in some of the hottest state of the art fields: Deep Learning, Artificial Intelligence, and Large Language Models. If you're a creative engineer with a real passion for robust and enjoyable user experiences, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Master's or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience
- 3+ years of professional experience writing production-grade asynchronous Python
- Deep experience with LangChain, Hugging Face libraries, vLLM, and SGLang
- Experience with TensorFlow, PyTorch, and Scikit-learn
- Proficiency in data analysis using Python, pandas, NumPy, or similar tools
- Hands-on experience with production model deployment, monitoring, performance analysis, and scaling using Kubernetes, Ray, or Slurm
- Strong understanding of GPU memory management and infrastructure tuning for high-throughput, low-latency AI inference
- Advanced knowledge of GitLab pipelines, automated testing, and vulnerability scanner integration
- Expert familiarity with Python testing frameworks such as PyTest, mocking libraries, and automated AI test generation
- High proficiency in advanced Git workflows, cryptographic commit signing, and public/private repository mirroring
- Experience aligning or fine-tuning LLMs, including regular LLMs, VLMs, or any-to-text models
- Prior scientific research and publication experience
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
