LLM Research Scientist – Pre-training, Computer Vision, Adversarial Robustness
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Job Description
LLM research scientist training and improving vision and language models for Mercor’s AI lab and enterprise partners. Focusing on adversarial robustness, computer vision, generative modeling, and multilingual pre-training.
Responsibilities:
- Train image classifiers and generative image models from scratch
- Fine-tune open-weight language models
- Optimize models under limited data, compute, and model-size budgets
- Improve model robustness against adversarial inputs and conversations
- Compress models to meet size and latency constraints while preserving accuracy
- Diagnose and resolve training issues
- Conduct empirical, open-ended machine learning research
- Collaborate with leading AI researchers on high-impact projects
- Work on projects training and enhancing AI systems for leading AI labs and enterprises
Requirements:
- 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
- Expertise in one or more of adversarial robustness, efficient computer vision, generative image modeling, LLM post-training and behavioral robustness, or multilingual pre-training
- Experience with adversarial training of image classifiers, robust accuracy evaluation, and robustness–accuracy trade-offs
- Experience training image classifiers end-to-end, model compression, and deployment under hard size or latency budgets
- Experience training diffusion models, GANs, VAEs, or flow-based generative models from scratch
- Experience with supervised fine-tuning and preference optimisation of open-weight language models
- Experience shaping conversational behavior and alignment-style fine-tuning
- Experience training multilingual or low-resource-language models from scratch and designing tokenizers across scripts
- Additional experience in scaling laws, curriculum learning, model evaluation, uncertainty estimation, calibration, or synthetic data is a plus
- Must be an independent contractor
- Mercor is unable to support H1-B or STEM OPT candidates at this time
Benefits:
- Flexible, project-based work
- Competitive compensation
- Fully remote work
- Work on your own schedule
- Weekly payments via Stripe or Wise
- Projects may be extended, shortened, or concluded early depending on needs and performance
- Reasonable accommodations upon request
- Referral compensation of up to $480 per successful referral
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