AI Researcher - End-to-End ML, Robustness & Efficiency

Mercor; San Francisco (CA); Remote; USD 150,000 - 210,000; Full time

Job summary

Mercor is seeking experienced machine learning researchers to train and improve deep learning models end-to-end across vision and language. You'll tackle well-scoped empirical open-ended ML research problems and collaborate with a team of leading AI researchers.

Responsibilities include training image classifiers and generative models, optimizing data/compute efficiency, and improving robustness against adversarial inputs while meeting latency and size constraints.

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 or FAANG/comparable research track record

Responsibilities

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

Skills

Adversarial Robustness

Efficient CV

Generative Image Modeling

LLM Fine-Tuning

Multilingual Pre-training

Education

PhD in ML/CS

Bachelor’s in CS or related

Tools

PyTorch

JAX

TensorFlow

CUDA

Job description

Mercor is seeking experienced machine learning researchers to train and improve deep learning models end-to-end across vision and language. You'll tackle well-scoped empirical open-ended ML research problems and collaborate with a team of leading AI researchers.

Responsibilities include training image classifiers and generative models, optimizing data/compute efficiency, and improving robustness against adversarial inputs while meeting latency and size constraints.