Adversarial Robustness ML Scientist – Efficient DL

Mercor; San Diego (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Mercor in San Diego is seeking experienced machine learning researchers to train and improve deep learning models end-to-end, across vision and language domains. You’ll tackle well‑scoped empirical ML research problems with real‑world impact.

You will work with PyTorch/JAX/TensorFlow, push robustness, compression, and efficiency, and contribute to open‑ended projects spanning adversarial robustness, multilingual pre‑training, and generative modeling.

Qualifications

• 3+ years of machine learning research experience (PhD counts).
• Strong experience with PyTorch, JAX, TensorFlow, or similar ML frameworks.
• Degree from a top‑100 university, FAANG or comparable AI company experience, 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 adversarial conversations.
• Compress models to meet hard size and latency constraints without sacrificing accuracy.
• Diagnose and resolve training issues.

Skills

PyTorch

JAX

TensorFlow

ML frameworks

Education

Top university degree or FAANG-level track

Job description

Mercor in San Diego is seeking experienced machine learning researchers to train and improve deep learning models end-to-end, across vision and language domains. You’ll tackle well‑scoped empirical ML research problems with real‑world impact.

You will work with PyTorch/JAX/TensorFlow, push robustness, compression, and efficiency, and contribute to open‑ended projects spanning adversarial robustness, multilingual pre‑training, and generative modeling.