Machine Learning Engineer AI Safety LLM MLOps @ NVIDIA
placeSanta Clara, CA
attach_money $190,000
businessOn Site
scheduleFull Time
Posted 12 hours ago
Your Application Journey
Interview
Email Hiring Manager
****** @nvidia.com
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Job Details
About the Role
Nvidia is seeking a talented Machine Learning Engineer AI Safety LLM MLOps to work on Product Security, Content Safety, ML Fairness, and Robustness adventures for large language models (LLMs). The position involves assessing, quantifying, and enhancing the safety and inclusivity of LLMs across research and production engineering teams.
Key Responsibilities
- Develop datasets and models for training and evaluating content safety and ML fairness systems.
- Research and implement cutting-edge techniques for bias detection and mitigation, including work with LLMs and retrieval-augmented generation (RAG) systems.
- Define and track key metrics for responsible LLM behavior and usage.
- Apply best MLOps practices including automation, monitoring, scalability, and safety.
- Contribute to the MLOps platform and build safety tools for ML teams.
- Collaborate with engineers, data scientists, and researchers to solve content safety and fairness challenges.
Basic Qualifications
- Master’s or PhD in Computer Science, Electrical Engineering or related field, or equivalent experience.
- 3+ years' experience in developing and deploying machine learning models in production.
- Strong understanding of machine learning principles and algorithms.
- Programming proficiency in Python and knowledge of frameworks like Keras or PyTorch.
- Experience in Content Safety, ML Fairness, Robustness, or AI Model Security.
- Background in addressing issues such as hate/harassment, sexualized content, or harmful/violent material.
- Experience with large multi-modal datasets and models.
- Excellent problem solving, collaboration, and communication skills.
Preferred Qualifications
- Experience with alignment/fine-tuning of LLMs, including vision language models.
- Background with multimodal/multilingual content safety and regulatory compliance.
- Experience in addressing issues like hallucinations, generative misinformation, and adversarial robustness.
- Demonstrated passion for AI, evidenced by research and publications.
About Nvidia
Nvidia is a leader in the technology space with a forward-thinking team working in deep learning, artificial intelligence, and large language models. With competitive salaries, equity, and benefits, Nvidia is renowned as one of the industry's most desirable employers.
Key skills/competency
Machine Learning, AI Safety, LLM, MLOps, Content Safety, Fairness, Robustness, Python, PyTorch, Keras
How to Get Hired at NVIDIA
🎯 Tips for Getting Hired
- Customize your resume: Highlight relevant ML safety and fairness experience.
- Research Nvidia's projects: Understand their AI safety and LLM initiatives.
- Prepare technical examples: Showcase practical MLOps and programming skills.
- Practice behavioral questions: Use clear, precise communication examples.
📝 Interview Preparation Advice
Technical Preparation
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Brush up on Python and ML frameworks.
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Practice building and deploying ML models.
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Review MLOps automation and monitoring techniques.
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Study bias detection methods and safety metrics.
Behavioral Questions
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Describe a past team project success.
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Explain conflict resolution in technical teams.
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Detail a challenge in model safety implementation.
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Share experiences collaborating across departments.
Frequently Asked Questions
What qualifications does Nvidia expect for the Machine Learning Engineer AI Safety LLM MLOps role?
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How important is experience in MLOps for this Nvidia role?
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Can experience with multimodal datasets enhance my application for this role at Nvidia?
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How does Nvidia view research and publication experience for this Machine Learning Engineer role?
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What soft skills are essential for success in this role at Nvidia?
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