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# Machine Learning Engineer for AI Model Evaluation

- **Company:** Johns Hopkins Applied Physics Laboratory
- **Location:** Washington, DC, United States (Remote available)
- **Experience:** Expert
- **Salary:** $85,000.0 - $195,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cursor (Graphical User Interface Elements), Machine Learning, Large Language Models, Model Validation, Machine Learning Operations
- **Published:** August 4, 2026
- **Apply:** https://www.careerjet.com/job/register/us3f4bfacbde33f1a593679abdddedc943

## About the Role

* Minimum 2 years of professional machine learning engineering experience.
 * Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products.
 * Regular, hands-on use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
 * Ability to evaluate model-generated machine learning implementations and reason about technical tradeoffs.
 * Experience deploying ML systems to production is preferred., * You must meet the qualifications above, including the minimum 2 years of ML engineering experience.
 * Regular use of AI coding agents is required, and experience evaluating model-generated implementations is essential.

## Description

Contribute to a frontier code agents project run with a leading AI research lab, evaluating and improving state of the art AI coding models via structured technical assessments. The work centers on realistic machine learning engineering workflows, model evaluation, and comparing outputs from multiple frontier models. Spots are limited and filling quickly on a first come, first serve basis. Key Responsibilities

 * Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
 * Review model-generated implementations that involve model training, inference systems, MLOps, and large language model applications.
 * Identify bugs, edge cases, performance regressions, and failure modes in model outputs and implementations.
 * Compare and contrast outputs from multiple frontier models, assessing strengths, weaknesses, and tradeoffs.
 * Apply professional engineering judgment to realistic ML engineering scenarios and provide clear, actionable evaluations.


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