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# Machine Learning Engineer, Frontier AI Evaluation (Contract)

- **Company:** Cobalt LLP
- **Location:** Piedmont, CA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Debugging, Python (Programming Language), Machine Learning, Pytorch, Deep Learning, Machine Learning Operations, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://www.disabledperson.com/jobs/74813014-machine-learning-engineer-frontier-ai-evaluation-contract

## About the Role

This opportunity is suited to practitioners rather than only researchers: ML engineers, applied scientists, MLOps and platform engineers, and data engineers who have trained, deployed, and maintained models in production. A PhD is welcome but not required, and hands-on delivery experience counts for more here than publication record.

You do not need prior experience in data annotation or AI research. What matters is that you can diagnose why a pipeline or a training run is failing, decide what the right fix is, and explain both clearly enough for another engineer to follow., * Several years of hands-on experience building, training, and deploying machine learning systems in production, with a track record you can point to

 * Strong coding ability in Python, plus working command of at least one deep learning framework such as PyTorch or JAX, and comfort reading unfamiliar codebases
 * Depth in at least one area, for example large-scale training and distributed compute, data pipelines and feature infrastructure, model serving and inference optimization, evaluation and monitoring, or fine-tuning and post-training workflows
 * Solid debugging discipline, including the ability to isolate a failure across data, model, and infrastructure rather than guessing at it
 * Ability to explain each step of your reasoning clearly in writing, and to produce work another engineer could reproduce and review

## Description

Cobalt is seeking machine learning engineers to produce the expert reasoning, task environments, and evaluation data used to train and assess frontier AI models on real ML engineering work., * Produce written reasoning traces on real ML engineering tasks, capturing how you diagnose a failing training run, a data pipeline defect, or a serving regression, including what you rule out and why

 * Author non-trivial ML engineering problems and task environments with checks that verify success automatically, including multi-file and multi-step tasks
 * Evaluate model-generated ML code and configurations, ranking solutions, explaining what makes the stronger one stronger, and identifying the point at which the approach goes wrong
 * Assess whether a proposed solution actually addresses the failure, and identify fixes that pass the immediate check but mask the underlying problem, degrade performance, or would not survive review
 * Design rubrics and partial-credit criteria for scoring multistep engineering tasks, and classify observed failures into a consistent taxonomy

Projects follow their own guidelines, formatting conventions, and quality standards, and you will work with feedback from reviewers and lab research teams.


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