Scientific / ML Engineer

Overview

Join a leading AI lab’s GenAI team to help build foundational AI models and improve the quality of AI training data. This role is focused on machine learning, deep learning, and scientific computing work that supports model training, evaluation, and technical task design.

This opportunity is presented by Mercor. The source also notes that the work is associated with Cincinnatus LLC as the employer of record for W-2 employment, payroll, benefits, and compliance, while placing workers within client teams.

What You'll Do

• Support research and engineering teams by helping close knowledge gaps in machine learning, model training and evaluation, and numerical/scientific software.
• Design challenging, domain-relevant tasks and write accurate solutions for machine learning and scientific computing problems.
• Review ML and scientific computing tasks and solutions, then provide clear written technical feedback.
• Create and evaluate technical tasks covering model training, experiment design, data pipelines, and scientific simulations, including runnable reference solutions.
• Develop rubrics to assess methodological soundness, correctness, and reproducibility.
• Work with subject matter experts to improve consistency and accuracy across training data.

Requirements

• At least 4 years of dedicated professional or research experience in machine learning, data science, or scientific computing at a recognized top-tier organization or research university; an MS or PhD in a quantitative field can count toward this requirement.
• Hands-on production or research experience with PyTorch, JAX, or TensorFlow.
• Strong Python skills, including NumPy, pandas, and scikit-learn.
• Publications, open-source contributions, or shipped ML systems are considered a strong plus.
• Demonstrable career progression.
• Availability for at least 20 hours per week during weekdays.
• Strong written communication skills and the ability to explain complex technical decisions clearly.

Contract & Work Arrangement

• Fully remote role.
• Work can be completed on your own schedule.
• Projects may be extended, shortened, or concluded early depending on needs and performance.
• Payments are made weekly on Stripe or Wise based on services rendered.
• The source states that H1-B and STEM OPT candidates cannot be supported at this time.