LLM Research Scientist – Pre-training, Computer Vision, Adversarial Robustness

🔥 12 hours ago

🇺🇸 United States – Remote

💵 $100 - $120 / hour

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

👻 Ghost score 9%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Mercor

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2023

🔥 Funding within the last year

💰 $350M Series C - Mercor on 2025-10

Mercor is a company for which no descriptive text was provided in the input. Additional information (products, services, target customers, or industry specifics) is needed to create an accurate summary and select appropriate industries.

📋 Description

• Train image classifiers and generative image models from scratch • Fine-tune open-weight language models • Conduct empirical, open-ended machine learning research • Optimize models under limited data, compute, and model-size budgets • Improve robustness against adversarial inputs and adversarial conversations • Compress models to meet size and latency constraints without sacrificing accuracy • Diagnose and resolve training issues • Collaborate with leading AI researchers on challenging, 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 • Experience with adversarial training of image classifiers, robust accuracy evaluation, and robustness–accuracy trade-offs • Experience with model compression, including quantization, pruning, and knowledge distillation • Experience deploying models under hard size or latency budgets • Experience training diffusion models, GANs, VAEs, or flow-based generative models from scratch • Experience with FID and training-efficiency techniques for generative models • Experience with supervised fine-tuning and preference optimisation (DPO, RLHF, RLAIF) of open-weight language models • Experience building datasets via synthetic generation, noisy or weak supervision, and rejection sampling • Experience shaping conversational behaviour over multiple turns • Experience with alignment-style fine-tuning while preserving general capability • Experience training multilingual or low-resource-language models from scratch • Experience with tokenizer design across scripts and typologically diverse languages • Experience balancing unequal per-language data • H1-B and STEM OPT candidates are not supported

🏖️ Benefits

• Flexible, project-based work • Competitive compensation • Fully remote role • Work on your own schedule • Weekly payments via Stripe or Wise • Projects can be extended, shortened, or concluded early depending on needs and performance • Collaboration with leading AI researchers • Reasonable accommodations upon request