Scientific Coding Expert - Material science_180 USD

Prointegrate World It Consulting; United States; Remote; USD 39,000 - 44,000; Full time

Job summary

Turing, based in San Francisco, seeks a SciCode Trainer to author, implement, and review complex scientific problems across STEM disciplines for frontier AI models.

You will design problem specifications, implement solutions in Python, and run QC checks to ensure high-quality training data and evaluation benchmarks across multiple LLM judges.

Qualifications

• Master's or PhD in Materials Science or related field.
• Strong Python programming skills with experience in scientific computing.
• Attention to detail with strict rubrics for well-posedness, test case discriminativeness, scientific correctness, and determinism.
• Experience in AI data annotation, research, or scientific writing; familiarity with LLM evaluation frameworks or coding benchmarks.

Responsibilities

• Write scientific problem specifications with one main problem and at least 3 sub-problems, all logically connected.
• Implement verified golden solutions in Python with complete unit tests.
• Design discriminative test cases and run QC validation on the platform.
• Participate in sync calls for reviews and project standups during overlap hours.

Skills

Python programming

Scientific computing

Education

Master's or PhD in Materials Science or related field

Tools

NumPy

SciPy

SymPy

Job description

About Turing:

Based in San Francisco, California, Turing is the worlds leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L

Rate

Talent Rate: $180/task upon approval

Complex task expected to take 6 hours to complete

Skill

• Master's or PhD in Material science or related field. Strong Python programming skills with experience in scientific computing
• Ability to write rigorous, well-posed scientific problems with clear constraints and expected outputs

Availability

8 hours per day with an overlap of 4 hours with PST.

Role Overview

Turing is building one of the most rigorous STEM AI training datasets in the industry. The SciCode project involves creating high-quality scientific coding tasks that are used to train and evaluate frontier AI models. As a SciCode Trainer, you will be directly contributing to cutting-edge AI research by authoring, implementing, and reviewing complex scientific problems across core STEM disciplines.

Responsibilities

• Write scientific problem specifications consisting of one main problem and a minimum of 3 sub-problems, all logically connected and progressively building toward the main problem solution Implement verified golden solutions in Python with complete unit test coverage
• Design discriminative test cases that clearly differentiate correct from incorrect model outputs Run QC validation checks on the Turing Central Task Platform (CTP) including Tier 1 structure checks and Tier 2 quality rubrics Iterate on tasks based on QC feedback to meet Pass@K evaluation criteria across multiple LLM judges (GPT, Gemini, Nemotron)
• Maintain high output quality with a low rework rate, targeting consistent L1 approval on first submission
• Participate in sync calls for reviews, feedback sessions, and project standups during overlap hours

Required Qualifications

• Master's or PhD in Material science or related field. Strong Python programming skills with experience in scientific computing
• Ability to write rigorous, well-posed scientific problems with clear constraints and expected outputs
• Attention to detail - tasks must meet strict rubrics for well-posedness, test case discriminativeness, scientific correctness, and determinism
• Prior experience in AI data annotation, research, or scientific writing Familiarity with LLM evaluation frameworks or coding benchmarks Experience with libraries such as NumPy, SciPy, SymPy, or domain-specific scientific tools . Published research or academic project experience in a STEM domain Quality Standards

Offer Details

• Commitments Required : Overlap of 4 hours with PST and 40 hrs/week
• Engagement type : Contractor assignment/freelancer (no medical/paid leave)
• Duration of contract : 8 weeks - Freelancing