Scientific / ML Engineer

$90–140/hr

rate as listed

Workload: 20+ hrs/week
Engagement: Project-based
Location: US
Field: STEM and Coding

Design and evaluate machine learning and scientific computing tasks used to improve AI training data, including runnable solutions and reproducibility rubrics. The role requires substantial research or professional experience, strong Python skills and hands-on work with PyTorch, JAX or TensorFlow.

The work

You will create technically challenging machine learning and scientific computing problems, implement runnable reference solutions and evaluate other solutions for correctness. Assignments may cover model training, experiment design, data pipelines, numerical methods and scientific simulations. You will also write rubrics that distinguish methodological soundness from results that merely appear plausible.

What the screen measures

The platform will look for at least four years of relevant professional or research experience, with an MS or PhD in a quantitative field counting toward that experience. Expect detailed follow-ups about required Python work using NumPy, pandas and scikit-learn, plus production or research experience with PyTorch, JAX or TensorFlow. Publications, open-source contributions and shipped ML systems are strong pluses rather than stated requirements. The screen may also ask for evidence of career progression and test whether you can explain technical judgments clearly in writing.

Working arrangement

This is a W-2 position with Cincinnatus LLC, which acts as the employer of record and may place successful candidates with a leading AI lab as part of its extended workforce. Candidates must be able to work reliably for at least 20 hours/week during weekdays. The listing does not specify whether the engagement is remote, fully asynchronous or tied to particular working hours, so confirm those details during the placement process.

Pay band: $90–140/hr

What they look for

At least four years of professional or research experience in machine learning, data science or scientific computing, with qualifying graduate study counted toward that experience
Strong Python using NumPy, pandas and scikit-learn
Production or research experience with PyTorch, JAX or TensorFlow
Experiment design, model training and evaluation
Scientific computing, numerical methods or scientific simulation
Technical task design, rubric development and written evaluation

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This checks whether the experience threshold is supported by a concrete timeline, relevant institutions and substantive technical ownership rather than adjacent analytics work.
A strong answer: Give a dated progression of roles, research appointments or qualifying graduate study, naming the systems, experiments or scientific problems you owned. Separate direct ML or scientific computing work from general software engineering or coursework.
Where people lose it: Candidates lose points by giving a résumé summary without dates, personal responsibilities or enough detail to verify that the work was dedicated to the relevant fields.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

STEM AND CODING on Sidequest

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