Machine Learning Engineer, AI Safety

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

Machine Learning Engineer focused on AI safety for multi-modal LLMs, including content safety, bias detection, mitigation, and robustness. The role involves developing datasets and models for training and evaluation, implementing cutting-edge techniques, defining metrics, and contributing to MLOps and safety tools. Requires experience in ML model deployment, Python, ML frameworks, and specific areas like Content Safety or ML Fairness.

What you'd actually do

  1. Develop the datasets and models for training and evaluating models and end-to-end systems for Content Safety, ProdSec, Robustness and ML Fairness.
  2. Research and implement cutting-edge techniques for bias detection and mitigation in LLMs and systems using LLMs like RAGs.
  3. Define and track key metrics for responsible LLM behavior and usage.
  4. Follow the best MLOps practices of automation, monitoring, scale and safety.
  5. Contribute to the MLOps platform and develop safety tools to help ML teams be more effective.

Skills

Required

  • Master’s or PhD in Computer Science, Electrical Engineering or related field - or equivalent experience.
  • Strong understanding of machine learning principles and algorithms.
  • Hands-on programming experience in python
  • in-depth knowledge of machine learning frameworks, like Keras or PyTorch.
  • Good at problem-solving and analytical ability.
  • Excellent collaboration and communication skills.

Nice to have

  • Skilled with alignment/fine-tuning of LLMs - including regular LLMs as well as VLMs (vision-language models) or any-to-text
  • Proven experience with multimodal and/or multilingual content safety, legal, and regulatory compliance.
  • Knowledge of robustness, including hallucinations, digressions, and generative misinformation.
  • Experience with GenAI security, including prompt stability, model extraction, confidentiality/data extraction, integrity, availability, and adversarial robustness.
  • Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research, and publication experience.

What the JD emphasized

  • Minimum of 2+ years of work experience in developing and deploying machine learning models in production.
  • Background in one or more of the following broader areas for 1+ years: Content Safety, ML Fairness, Robustness, AI Model Security, or related areas.
  • Practice working with large multi-modal datasets and multi-modal models.

Other signals

  • developing AI-based products
  • ensuring the highest Content Safety possible
  • Preventing Bias and Discrimination
  • assessing, quantifying, and improving the safety and inclusivity of our LLM models in a scalable fashion