Machine Learning Engineer
Santa Clara, CA, US; CA, US
- Pay
USD 152,000–241,500/year · BaseAnnual period assumed · Location-specific pay — pay source
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.
Read the full posting- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingYou will manage GPU orchestration, prompt-tune models, and build advanced AI workflows using platforms such as Kubernetes, Ray, or Slurm.
From the employer’s posting
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:
Education & alternatives
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.
Tools in this posting
- Python
- Kubernetes
- NumPy
- pandas
- PyTorch
- TensorFlow
- scikit-learn
- Huggingface
Source — Tool mentions in context
* 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
* 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.
* 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.
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:
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.
* 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.
* 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.
Job description
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
Complete your application on jobs.nvidia.com. The employer’s form will show what is required.
Already applied? Track this application
Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
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.
- Location & working pattern
Santa Clara, CA, US; CA, US
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Sep 13, 2026
- Recorded sightings
- 13
- Last seen by us
- Oct 8, 2026
- Employer says posted
- Sep 8, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Report an errorSee how this role fits your experience
Add your resume to compare the role’s scope, tools and requirements with your experience.