> Markdown version of [/jobs/ext/1319095-lead-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/1319095-lead-machine-learning-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). --- # Lead Machine Learning Engineer - **Company:** Empower Professionals - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $145,600.0 - **Contract:** Temporary contract - **Skills:** Big Data, Cloud Computing, Github, Python (Programming Language), Machine Learning, Azure Machine Learning, SQL Databases, Systems Architecture, Pytorch, Snowflake, Model Validation, Pandas, Scikit Learn, Xgboost, Machine Learning Operations, Docker - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/3c0b9583-5188-4027-9dfe-f3648e55f113 ## About the Role Advanced Python development for machine learning (Pandas, Scikit-learn). Experience building and deploying ML models from prototype to production. Strong expertise in SQL and large-scale data processing. Experience with cloud-based ML platforms (Azure Machine Learning preferred). Machine learning model development using XGBoost, LightGBM, or PyTorch., Experience in utilities or other regulated industries. Background in optimization and operations research. Experience leading small technical teams. In compliance with the salary transparency law, the expected pay range for this role is $70/hr. Actual compensation depends on experience and interview evaluation ## Description The Lead Machine Learning Engineer will be responsible for designing and owning the end-to-end machine learning architecture and recommendation engine framework. This role will lead the development of scalable ML solutions, guide technical direction, and drive the transition of models from experimentation to production., Design and implement machine learning system architecture and recommendation engine solutions. Lead model development, deployment, and productionization efforts. Collaborate with data scientists, engineers, and business stakeholders to define technical solutions. Establish ML engineering best practices, model governance, and deployment standards. Provide technical leadership to a small delivery team. Optimize model performance, scalability, and reliability., Python SQL Snowflake Azure Machine Learning GitHub Docker XGBoost/LightGBM/PyTorch ## Related Videos - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [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) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Software Engineer Salary London](https://www.wearedevelopers.com/magazine/252-software-engineer-salary-london) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [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)