Adversarial ML Scientist: Robustness & Efficiency

Mercor; San Francisco (CA); On-site; USD 180,000 - 235,000; Full time

Job summary

Mercor is seeking experienced machine learning researchers to advance deep learning models across vision and language, training end-to-end and improving empirical open-ended ML research outcomes. You will take ownership of scalable training, robustness, and model compression while addressing hard constraints on data, compute, and latency.

The role emphasizes hands-on experimentation with adversarial robustness, efficient computer vision, and multilingual pre-training, alongside LLM post-training

Qualifications

• 3+ years of machine learning research experience (PhD research counts toward this requirement).
• 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 track record through publications or impactful open-source contributions.

Responsibilities

• Train image classifiers and generative image models from scratch, and fine-tune open-weight language models.
• Get the most out of limited data, compute, and model-size budgets.
• Make models robust — to adversarial inputs and to adversarial conversations.
• Compress models to meet hard size and latency constraints without sacrificing accuracy.
• Diagnose and resolve training issues.

Skills

Adversarial Robustness

Efficient Computer Vision

Generative Image Modeling

LLM Post-Training & Behavioral Robust

Multilingual Pre-training

Education

PhD research counts toward requirement

Tools

PyTorch

JAX

TensorFlow

Job description

Mercor is seeking experienced machine learning researchers to advance deep learning models across vision and language, training end-to-end and improving empirical open-ended ML research outcomes. You will take ownership of scalable training, robustness, and model compression while addressing hard constraints on data, compute, and latency.

The role emphasizes hands-on experimentation with adversarial robustness, efficient computer vision, and multilingual pre-training, alongside LLM post-training