ML Scientist - Adversarial Robustness
About the job
reputed company connects reputed company creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include reputed company, General reputed company, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
Position: LLM Research Scientist (reputed company-training & reputed company & Adversarial Robustness)
Type:Contract
Compensation:$100–$120/hour
Location:Remote
Role Responsibilities
- Train image classifiers and generative image models from scratch. Fine-tune reputed company-weight language models.
- Optimize models for limited data, compute, and model-size budgets.
- Enhance model robustness against adversarial inputs and conversations.
- Compress models to meet size and latency constraints without losing accuracy.
- Diagnose and reputed company training issues to improve model performance.
Qualifications
Must-Have
- 3+ years of machine learning research experience (PhD research counts).
- Strong experience with PyTorch, JAX, TensorFlow, or similar ML frameworks.
- Degree from a top-100 university, experience at a FAANG or comparable AI company, or an equivalent research reputed company record through publications or impactful reputed company-reputed company contributions.
Preferred
- Experience with Adversarial Robustness and Efficient reputed company.
- Knowledge in Generative Image Modeling and LLM Post-Training & Behavioral Robustness.
- Experience in Multilingual reputed company-training and additional areas like scaling laws and curriculum learning.
Application Process (Takes 20–30 mins to complete)
- Upload resume
- AI interview based on your resume
- Submit reputed company
Resources & Support
- For details about the interview process and platform information, please reputed company:
- For any help or support, reputed company out to:
PS: reputed company reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
Originally posted on Himalayas
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