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# Machine Learning Researcher

- **Company:** DIGITAL SKILLS LLC
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Open Source Technology, Pytorch, Deep Learning, Attention Mechanisms, Information Technology
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_bca61d5303cdb59456c080ed20feafc2

## About the Role

Previous finance experience is not required. This is an opportunity to apply research developed in technology or AI to a new domain., * Strong foundations in probability, statistics, algorithms and mathematical reasoning.

 * Hands-on experience developing, training and evaluating machine learning or deep learning models.
 * Proficiency in Python and PyTorch, with the ability to write clear, maintainable research code.
 * A rigorous approach to experimentation, including testing assumptions and explaining why an approach works.
 * Experience working with models in a production environment.
 * Typically 3-10 years of relevant experience, with flexibility based on research depth and delivery.
 * Ideally a PhD in mathematics, computer science, statistics or another relevant STEM discipline. A master's degree combined with strong relevant industry experience will also be considered., * Research into model architecture, attention mechanisms, mixture-of-experts models or long-context approaches.
 * Experience improving training stability or scaling larger models.
 * Carefully controlled experiments demonstrating measurable improvements in model quality.
 * A practical understanding of GPU memory, compute requirements and performance trade-offs.
 * Meaningful contributions to open-source model implementations or research code.

These are areas of interest rather than a requirement to cover every topic.

## Description

* Research and develop statistical and deep learning models to identify useful patterns in complex datasets.
 * Apply probability, estimation and regression methods to evaluate signals and test research ideas.
 * Build reproducible research workflows in Python and PyTorch.
 * Investigate model behavior, diagnose training instability and improve generalization.
 * Explore pre-training, fine-tuning, regularization and architectural choices.
 * Evaluate the trade-offs between model quality, computational cost and practical implementation.
 * Collaborate with researchers and engineers to turn successful research into production applications.