This is not a traditional science or engineering role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long-form and self-directed, and clear written reasoning matters as much as technical depth.
Responsibilities
Design challenging, realistic technical tasks drawn from your own day-to-day work: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an engineer or scientist would need (test data, drawings or schematics, specifications, simulation outputs, lab records, reports), which you author yourself.
Run those tasks through frontier AI models and evaluate the deliverable they produce (the calculation, analysis, design review or report) against the standard you would hold a colleague to.
Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short.
Write detailed grading rubrics that specify what a correct deliverable must contain (the right assumptions stated, the right method, the right magnitudes and units, the right failure modes considered), and explain in writing why a response passes or fails each one.
Flag concrete failures with evidence: unit and scaling errors, misread data, unsupported conclusions, fabricated or ignored source files, missed safety or boundary conditions, and off-brief interpretation of the ask.
Contribute across your discipline and adjacent ones, and review and refine tasks built by other experts.
Qualifications
2+ years of applied experience preferred in one of the core sciences (mathematics, physics, chemistry, or biology) or in an engineering discipline (electrical and electronics, civil and structural, materials, chemical and process, environmental and earth sciences, or similar) or in data science.
In progress Bachelor’s degree or higher. We prefer 2+ years of experience outside undergraduate study.
Coding / scientific computing is a strong plus and is required on some projects: comfortable writing and verifying work in code (Python or similar) and working at the command line.
Hands-on with real data and the tools of your field: instrument and test data, measurement files, simulation outputs, schematics or drawings, and the analysis tools that go with them (MATLAB, Python or R; SPICE and PCB tools; FEA or CFD; GIS; chromatography or spectroscopy software).
Comfort with statistical and experimental reasoning: experiment design, measurement error and uncertainty, and the common statistical traps. The assessment leans on this.
Working understanding of adjacent sub-disciplines, enough to assess work outside your own specialty and point out what was done correctly or incorrectly.
Hands-on practitioner: you currently do (or recently did) the work yourself at an individual-contributor level, not solely in a managerial capacity.
Full professional or native-level written and spoken English; you can articulate why a result is wrong, not only that it is.
General familiarity with AI and LLM tools: you can tell a well-reasoned answer from a plausible-sounding but incorrect one.
Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing).
Advanced degree or professional licensure (PE, chartered status) is a plus but not required. Practical applied work outweighs credentials.
