Senior AI Researcher: Vision, Language & Robustness

Mercor; Greater London; Remote; GBP 90,000 - 130,000; 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 domains. You will address empirical open-ended ML problems, optimize data, compute, and model size budgets, and enhance model robustness against adversarial inputs and conversations.

The role covers multilingual pre-training, model compression, and LLM post-training with strong emphasis on reproducibility and evaluation against standard metrics like FID and

Qualifications

• 3+ years of machine learning research experience (PhD 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 Robustn

Multilingual Pre-training

Data Augmentation

Uncertainty Estimation

Model Compression

Education

Top-100 university degree

PhD considered

Tools

PyTorch

JAX

TensorFlow

Job description

Mercor is seeking experienced machine learning researchers to train and improve deep learning models end-to-end across vision and language domains. You will address empirical open-ended ML problems, optimize data, compute, and model size budgets, and enhance model robustness against adversarial inputs and conversations.

The role covers multilingual pre-training, model compression, and LLM post-training with strong emphasis on reproducibility and evaluation against standard metrics like FID and