Verified Mercor opportunity
Mercor is seeking experimental scientists and engineers across inorganic synthesis, characterization, superconductors, and semiconductors (including advanced packaging) to support a frontier AI research lab building models for materials science and the physical sciences. This is hands-on, expert-level work: you'll apply deep, specialized knowledge to generate, structure, and evaluate the scientific data these models learn from — and your input will directly shape how advanced models reason about materials, devices, and processes. — Key Responsibilities: • Contribute domain expertise across synthesis, characterization, fabrication, and device physics to build high-quality training and evaluation data. • Review and evaluate AI-generated scientific reasoning, catching errors and improving technical accuracy. • Design and solve challenging, expert-level problems in your area of specialization. • Rate and rank model outputs against defined scientific criteria, with clear written reasoning. • Structure technical knowledge — experimental procedures, characterization results, process data — into well-organized, model-ready data. • Deliver reliable, high-quality work within defined timelines. — You're a strong fit if you have: • Hands-on experimental experience in one or more of: inorganic synthesis (solid-state, solution, solvothermal, sol-gel), superconducting materials, or semiconductors and advanced packaging. • Strong materials or device characterization skills (XRD, SEM, TEM, spectroscopy, electrical/transport measurements). • Experience with thin-film growth or device fabrication (MBE/epitaxy, MOCVD, CVD, sputtering, IBAD, etch, clean-room microfabrication) — a plus. • An advanced degree (PhD/MS) or equivalent hands-on experience in materials science, chemistry, physics, or a related engineering field. • Clear written English and the ability to explain technical reasoning concisely. — Role Details: • Type: Long-term, ongoing engagement • Engagement: Up to 40 hours/week (minimum 10) • Work arrangement: Remote (US-based)