Lead Machine Learning Engineer
$197,300–$225,100 year
On-siteNew York City, New York, United States or McLean, Virginia, United States
Job Summary
Design, build, and deliver machine learning models and components that solve real-world business problems while collaborating with Product and Data Science teams. Write and test application code to develop, validate, and automate the deployment of ML models, ensuring high availability and performance through rigorous testing and continuous integration practices. Retrain, maintain, and monitor models in production using cloud-based architectures to optimize data pipelines and feature selection. Manage code to reduce vulnerabilities and ensure models follow best practices in responsible and explainable AI.
Required Qualifications
- Bachelor's Degree
- At least 6 years of experience designing and building data-intensive solutions using distributed computing
- At least 4 years of experience programming with Python, Scala, or Java
Desired Qualifications
- Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
- 3+ years of experience building production-ready data pipelines that feed ML models
- 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
- 2+ years of experience developing performant, resilient, and maintainable code
- 2+ years of experience with data gathering and preparation for ML models
- 2+ years of people leader experience
- 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
- Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion
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