Remote Inorganic Materials Scientist for AI-Driven Materials Research

SaidGig; New York (NY); Remote; USD 98,000 - 134,000; Full time

Job summary

SaidGig is seeking a highly skilled materials scientist to develop training and evaluation data for AI models focused on inorganic materials, superconductors, semiconductors, and advanced packaging. You will apply hands-on experimental methods and structured data practices to generate high-quality, model-ready datasets.

This is a remote, US-based hourly engagement with 10–40 hours per week, long-term in scope.

Qualifications

• PhD or MS in materials science, chemistry, physics, or related engineering discipline; hands-on experience acceptable.
• Clear written English and ability to explain technical reasoning concisely.
• Experience with thin-film growth or device fabrication (MBE/epitaxy, MOCVD, CVD, sputtering, IBAD, etching, clean-room).

Responsibilities

• Contribute expertise in synthesis, characterization, fabrication, and device physics to create training and evaluation data.
• Review AI-generated scientific reasoning, identify errors, and improve technical accuracy.
• Design and solve challenging problems within your area of specialization.
• Rate and rank model outputs against defined scientific criteria, documenting clear written rationale.
• Organize experimental procedures, characterization results, and process data into structured, model-ready formats.
• Deliver reliable, high-quality work within established timelines.

Skills

Inorganic synthesis

Superconducting materials

Semiconductors & packaging

Materials characterization

XRD

SEM

TEM

Spectroscopy

Device physics

Written English

Education

PhD or MS in materials science/chemistry/physics or related field

Tools

MBE / epitaxy

MOCVD

CVD

Sputtering

Etching

Clean-room microfabrication

Job description

SaidGig is seeking a highly skilled materials scientist to develop training and evaluation data for AI models focused on inorganic materials, superconductors, semiconductors, and advanced packaging. You will apply hands-on experimental methods and structured data practices to generate high-quality, model-ready datasets.

This is a remote, US-based hourly engagement with 10–40 hours per week, long-term in scope.