2026 PhD / Master's Residency - Rapid Evaluation Techno-Economic Analysis (TEA)

The Project: Revolutionizing Technical Due Diligence

The Moonshot team at X is building the Agentic Techno-Economic Framework, an intelligent system designed to automate the evaluation of emerging technologies. This project aims to bridge engineering complexity and economic reality to accelerate the path from "what if" to "how soon".

Location

X's headquarters is located in Mountain View, CA.

Start Date(s)

The start date is on a year-round rolling basis.

Duration

The duration of the residency is a flexible 4 months to 1 year program based on project team needs and your availability.

What You Should Have

• Currently enrolled in a Masters or PhD program in engineering, science, or related field.
• Experience building comprehensive techno-economic analysis (TEA) and scenario models, ideally having used tools like Aspen HYSYS, Aveva, ChemCAD, DWSIM, etc.
• Proficiency in process modeling (mass/energy balances), financial modeling, and uncertainty quantification (e.g., Monte Carlo).
• Interest and passion in building moonshot technologies.

Additional Public Information

For more information, visit this video.

Compensation

The US base salary range for this position is $100,000 - $147,000 + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

About the Role

This internship offers the opportunity to be embedded into one of our confidential or public X projects, get paid competitively, and be part of a lively community of AI and ML Residents.

Qualifications

• We are looking for candidates who possess "moonshot thinking" and a desire to push the boundaries of traditional scientific modeling.
• Industry experience in physical or applied science fields either in job experience or through academic partnerships with industry is a plus.
• Comfort with ambiguity and a high "failure tolerance"—the ability to walk away from a project if the data suggests it isn’t promising.
• Interest or experience with using LLMs, agentic workflows, and application frameworks like Langchain, VertexAI, etc. to complement physical sciences research is a plus.

Skills

Knowledge of process modeling (mass/energy balances), financial modeling, and uncertainty quantification (e.g., Monte Carlo) is essential.

Benefits

Includes competitive pay and benefits.

Schedule

The schedule is flexible, based on project team needs and your availability.