Scientific / ML Engineer

What You'll Do6

• Spot gaps in what research and engineering teams know about model training, evaluation, and numerical software, then design work that closes them.
• Write demanding, domain-specific machine learning and scientific computing tasks with clean, well-structured reference answers.
• Review submitted ML and scientific computing solutions and give precise written technical feedback.
• Build and assess tasks that cover experiment design, data pipelines, model training and scientific simulations, each with a solution that actually runs.
• Create rubrics that test whether ML and scientific work is methodologically sound, correct and reproducible.
• Work alongside other subject matter experts to keep training data consistent and accurate.

Requirements6

• 4 or more years of focused professional or research work in machine learning, data science or scientific computing at a top-tier organization or research university. An MS or PhD in a quantitative field counts toward that total.
• Hands-on production or research use of PyTorch, JAX or TensorFlow.
• Strong Python skills with NumPy, pandas and scikit-learn.
• A track record of progressing through increasingly senior work.
• Availability for at least 20 hours per week on weekdays.
• Clear writing, with the ability to walk a reader through complex technical decisions.

Preferred Qualifications3

• Published research in ML or a related quantitative field.
• Open-source contributions to ML or scientific computing projects.
• ML systems shipped to production.

Who Should Apply

Researchers and engineers with 4+ years in machine learning or scientific computing who can reason about model training, experiment design and reproducibility, and who write clearly enough to defend those judgments in review. Candidates holding an MS or PhD in a quantitative field fit well, as do people who have shipped real ML systems.

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