Job Opportunities API

The Public Ledger of Openings

← Back to the ledger

Atomic Layer Deposition Scientist for AI Model Training

Saidgig
CompanySaidgig
CategoryData & Analytics
Location
Remote
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
First seen2 Aug 2026 (the employer did not state a posting date)
Last verified9 Aug 2026
SourceThe employer's own careers page (company_site)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
Role Overview Apply hands-on, expert-level atomic layer deposition and thin-film process knowledge to generate, structure, and evaluate scientific data that trains and validates advanced AI models for semiconductor and physical sciences applications. Your technical input will shape how models reason about deposition, materials, and semiconductor processing, by producing model-ready datasets, judging model outputs, and solving complex process problems. Key Responsibilities • Provide domain expertise in ALD process development, precursor chemistry, and thin-film characterization to produce high-quality training and evaluation data. • Review and evaluate AI-generated scientific reasoning, identify errors, and improve technical accuracy. • Design and solve challenging, expert-level problems in ALD and semiconductor processing. • Rate and rank model outputs against defined scientific criteria, documenting clear written reasoning for evaluations. • Structure technical knowledge, including process parameters, recipes, and characterization results, into well-organized, model-ready data. • Deliver reliable, high-quality work within agreed timelines. Qualifications • Hands-on experience developing, optimizing, or troubleshooting ALD processes. • Deep knowledge of thin-film deposition for semiconductor or advanced-packaging applications. • Strong understanding of precursor chemistry and surface reaction mechanisms. • Experience with materials characterization techniques such as XRD, SEM, TEM, XPS, ellipsometry, and similar methods. • Advanced degree (PhD or MS) or equivalent hands-on experience in materials science, chemistry, chemical engineering, or physics. • Clear written English and the ability to explain technical reasoning precisely. Work Terms • Engagement type, long-term and ongoing. • Time commitment up to 40 hours per week, with a minimum of 10 hours per week. • Work arrangement is remote, applicants must be based in the United States. • Work is hourly, hands-on, expert-level contribution to dataset creation and model evaluation. Compensation • Hourly rate: $84 per hour. Eligibility • Must be located in the United States to participate in this remote engagement. • Proficiency in written English is required for clear technical documentation and evaluation.