> Markdown version of [/jobs/ext/2710554-ml-engineer](https://www.wearedevelopers.com/jobs/ext/2710554-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Engineer - **Company:** MyFunded Futures, LLC - **Location:** United States - **Experience:** Starter - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Cluster Analysis, Continuous Integration, Information Engineering, Information Leak Prevention, Python (Programming Language), Logistic Regression, Machine Learning, Monte Carlo Methods, Standard Sql, Support Vector Machine, Workflow Management Systems, Cloud Platform System, Snowflake, Pyspark, Information Technology, Xgboost, K Means, Docker, Databricks - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/junior-machine-learning-engineer-myfunded-futures-9838669 ## About the Role * Bachelor's degree (or equivalent) in computer science, mathematics, engineering, or a related field, with coursework in machine learning or statistical learning. Graduate degree is a plus. * Strong Python and PySpark skills, with the ability to write clean, tested, maintainable code. * Hands-on experience with a cloud data platform (Databricks, Snowflake, Fabric, or similar) * Strong SQL, including window functions and multi-table joins. * Solid understanding of core ML concepts: cross-validation, overfitting, class imbalance, data leakage (including in time-ordered data), and choosing evaluation metrics appropriate to the problem. * Hands-on experience with: * Gradient-boosted trees (XGBoost, LightGBM) * Logistic regression, support vector machines, k-nearest neighbors * Clustering methods (k-means and others) Experience with some of the following: survival / time-to-event analysis, experiment design and causal inference, simulation and Monte Carlo methods, probability calibration, Bayesian or hierarchical modeling, model monitoring and drift detection Experience taking a model from development into a scheduled or production environment Docker, CI/CD, and workflow orchestration experience Ability to explain model behavior, including feature importance, calibration, and limitations. Ability to gather and present technical results to a non-technical audience. Proven experience as a machine learning engineer or in a similar role is a plus. Fintech, trading, or financial services background is a plus., Applicants must be authorized to work in the applicable country without employer sponsorship. The Company does not offer visa sponsorship or immigration assistance for this position. ## Description You will partner closely with the Data Science and Analytics team and with stakeholders across the organization, translating business questions into well-defined analytical problems and presenting results in terms decision-makers can act on. This role is ideal for someone early in their career who has already built and shipped machine learning models and who wants broader exposure across modeling, analytics, and data engineering., * Develop, test, validate, and maintain machine learning models under the guidance of senior team members. * Build and maintain data pipelines and analytical datasets on the Company's cloud data platform. * Evaluate model performance rigorously and document assumptions, methods, and limitations. * Support statistical analysis, forecasting, and experimentation to inform business decisions. * Present technical findings clearly to non-technical audiences. * Contribute to standards for model documentation, validation, and monitoring. ## Related Videos - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Logs in observability - Correlation](https://www.wearedevelopers.com/videos/1430-logs-in-observability-correlation) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs)