LLM Research Scientist – Pre-training, Computer Vision, Adversarial Robustness
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Job Description
LLM research scientist training robust vision and language models for Mercor’s frontier AI projects. Developing efficient, adversarially robust, and multilingual machine learning systems.
Responsibilities:
- Train image classifiers and generative image models from scratch
- Fine-tune open-weight language models
- Optimize models within limited data, compute, and model-size budgets
- Improve model robustness against adversarial inputs and adversarial conversations
- Compress models to meet hard size and latency constraints without sacrificing 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 focused on training and enhancing AI systems
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
- Strong 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 across multiple turns
- Experience training multilingual or low-resource-language models and tokenizer design across scripts
- Experience balancing unequal per-language data in data-constrained regimes
- Experience in scaling laws, curriculum learning, model evaluation, uncertainty estimation, calibration, data augmentation, or synthetic data is a plus
- Must be able to work as an independent contractor
- Mercor is unable to support H1-B or STEM OPT candidates
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
- Collaboration with leading AI researchers
- Referral payments of up to $480 for each successful referral, subject to limits
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