Role Description - Develop high-quality data sets by creating challenging problems in General Relativity, Astrophysics, and Cosmology. - Evaluate and improve AI model performance using rigorous physics expertise. - Collaborate with AI research teams to enhance the quality of training data. - Work independently and asynchronously to meet deadlines and contribute effectively. - Follow complex instructions to ensure the accuracy and depth of AI-generated content. Qualifications - PhD in Physics with specialization in General Relativity, Astrophysics, or Cosmology. - Graduate degree from US/UK/Canada/Western Europe. - High attention to detail. - Exceptional written and verbal communication skills. - Excellent proficiency in English. Requirements - Contract position. - Compensation: $70–$90/hour. - Location: Remote. - Commitment: 4–6 tasks/week. Company Description

Role Description - Write and refine prompts to guide model behavior in chemistry contexts. - Evaluate LLM-generated responses to chemistry-related queries for scientific accuracy and quantitative reasoning. - Conduct fact-checking using authoritative public sources and domain knowledge. - Annotate model responses by identifying strengths, areas of improvement, and inaccuracies. - Assess clarity, structure, and appropriateness of explanations for different audiences. - Apply consistent evaluation standards by following clear taxonomies and benchmarks. Qualifications - PhD in Chemistry or a closely related field - Deep expertise in Organic & Biological Chemistry, Inorganic & Materials Chemistry, Physical & Theoretical Chemistry, or Analytical & Instrumental Chemistry - Significant experience using large language models (LLMs) - Excellent writing skills and strong attention to detail - Experience reviewing or editing technical or academic writing - Prior experience with RLHF, model evaluation, or data annotation work (Preferred) - Experience teaching or explaining chemistry concepts to non-expert audiences (Preferred) - Familiarity with evaluation rubrics or structured review frameworks (Preferred) Company Description Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Role Description The Lead AI Research Scientist designs and analyzes machine learning experiments and develops and validates new AI Techniques, and collaborates with cross-functional teams to apply research to real use cases. This role supports model optimization, creates reusable research assets, and contributes to advancing the Company’s AI initiative. - Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics. - Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases. - Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness. - Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes. - Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints. - Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation. - Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems. - Contribute to academic publications and represent the company in research communities, as needed. Qualifications - Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred. - Candidates with a master’s degree and exceptional research or industry experience will also be considered. - 5–7 years of experience in AI/ML research roles, ideally in applied or product-focused environments. - Demonstrated success in delivering research-driven solutions that have been deployed in production. - Experience collaborating in cross-functional teams across research, engineering, and product. - Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus. - Strong foundational knowledge in machine learning and deep learning algorithms. - Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO). - Ability to read, implement, and adapt state-of-the-art research papers to real-world use cases. - Proficiency in hypothesis-driven experimentation, ablation studies, and statistically sound evaluations. - Advanced programming skills in Python (preferred), C++, or Java. - Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc. - Strong mathematical foundations in probability, linear algebra, and calculus. - Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc. - Ability to translate research insights into roadmaps, technical specifications, and product improvements. Requirements - 5 to 7 Years of relevant work experience. Benefits - Comprehensive benefits package designed to support the physical, emotional, and financial health of you and your family, including healthcare, time off, retirement, and well-being programs. - Professional development opportunities, including earning a professional certification relevant to your field and tuition reimbursement. - Quarterly and annual incentive programs for all employees who exceed expectations.

• Design, execute, and analyze machine learning experiments • Stay up to date with the latest AI research • Define rigorous evaluation protocols • Collaborate closely with domain experts and product teams • Develop reusable research assets