Machine Learning Engineer AI Safety LLM MLOps
@ NVIDIA

Hybrid
$180,000
Hybrid
Full Time
Posted 12 hours ago

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Job Details

About the Role

Nvidia is seeking a talented Machine Learning Engineer AI Safety LLM MLOps to drive efforts in product security, content safety, ML fairness and robustness for large language models. This role focuses on assessing, quantifying, and improving safety and inclusivity of LLMs with scalable solutions.

What You'll Be Doing

  • Develop datasets and models for training and evaluation for content safety and ML fairness.
  • Research and implement techniques for bias detection and mitigation in LLMs.
  • Define and track key metrics for responsible LLM behavior and usage.
  • Follow best MLOps practices including automation, monitoring, and scaling.
  • Contribute to the MLOps platform and develop safety tools for ML teams.
  • Collaborate across engineering, data science, and research teams.

What We Need To See

Master’s or PhD in a relevant field or equivalent experience. Minimum 3 years of ML model production experience with strong fundamentals in machine learning. Proficiency in Python and ML frameworks like Keras or PyTorch is required. Experience in Content Safety, ML Fairness, Robustness or AI Model Security with hands-on exposure to handling large multi-modal datasets is a plus.

Ways To Stand Out From The Crowd

Experience with LLM fine-tuning including Vision Language Models, multimodal/multilingual content safety, and GenAI security practices is highly valued.

Key skills/competency

  • Machine Learning
  • AI Safety
  • Content Safety
  • ML Fairness
  • Robustness
  • MLOps
  • LLMs
  • Python
  • Data Science
  • Bias Mitigation

How to Get Hired at NVIDIA

🎯 Tips for Getting Hired

  • Research NVIDIA's culture: Understand their innovative AI and safety focus.
  • Customize your resume: Highlight ML and safety expertise.
  • Tailor your skills: Emphasize Python, Keras, PyTorch experience.
  • Prepare for interviews: Focus on MLOps and bias mitigation topics.

📝 Interview Preparation Advice

Technical Preparation

Review ML algorithms and bias detection methods.
Practice coding in Python and ML frameworks.
Study MLOps best practices and automation strategies.
Analyze case studies on LLM safety and scaling.

Behavioral Questions

Describe a time you solved a complex problem.
Explain how you collaborate under pressure.
Discuss a challenge in team communication.
Share experience handling project uncertainty.

Frequently Asked Questions