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

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop image classifiers and generative image models from the ground up.

• Refine open-weight language models through fine-tuning.

• Optimize models while adhering to constraints on data, computational resources, and model size.

• Enhance model resilience against adversarial inputs and conversations.

• Compress models to adhere to strict size and latency requirements without compromising accuracy.

• Identify and resolve training-related issues.

• Engage in empirical, exploratory machine learning research.

• Collaborate with top AI researchers on impactful projects.

• Participate in initiatives aimed at training and improving AI systems.

⛳️ Requirements

• Minimum of 3 years of experience in machine learning research; PhD research qualifies.

• Extensive experience with PyTorch, JAX, TensorFlow, or similar machine learning frameworks.

• A degree from a top-100 university, experience at a FAANG or similarly recognized AI firm, or an equivalent research background demonstrated through publications or significant open-source contributions.

• Strong knowledge in one or more areas such as adversarial robustness, efficient computer vision, generative image modeling, post-training of large language models, behavioral robustness, or multilingual pre-training.

• Experience with adversarial training of image classifiers, evaluation of robust accuracy, and understanding robustness-accuracy trade-offs.

• Proficient in end-to-end training of image classifiers, model compression, and deployment under strict size or latency constraints.

• Familiarity with training diffusion models, GANs, VAEs, or flow-based generative models from scratch.

• Experience in supervised fine-tuning and preference optimization of open-weight language models.

• Skilled in shaping conversational behavior over multiple interactions.

• Background in training multilingual or low-resource language models and designing tokenizers across different scripts.

• Ability to balance unequal data across languages in data-constrained environments.

• Knowledge in scaling laws, curriculum learning, model evaluation, uncertainty estimation, calibration, data augmentation, or synthetic data is advantageous.

• Must be capable of functioning as an independent contractor.

• Mercor is unable to sponsor H1-B or STEM OPT candidates.

🏝️ Benefits

• Flexible, project-based work.

• Competitive pay.

• Fully remote work arrangement.

• Ability to set your own schedule.

• Weekly payments through Stripe or Wise.

• Projects may be adjusted in length or concluded early based on needs and performance.

• Reasonable accommodations available upon request.

• Opportunities for collaboration with leading AI researchers.

• Referral bonuses of up to $480 for each successful referral, subject to certain limits.