Sr. Lead Machine Learning Engineer
$150,000–$200,000 year
On-siteNew York City, New York, United States or Richmond, 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. Inform infrastructure decisions using ML modeling techniques, including feature selection, model training, and validation. Solve complex problems by writing and testing application code, developing ML models, and automating tests and deployment. Retrain, maintain, and monitor models in production, leveraging cloud-based architectures to deliver optimized ML models at scale. Construct data pipelines to feed models and ensure code is well-managed to reduce vulnerabilities while following Responsible and Explainable AI best practices. Work within a cross-functional Agile team to create and enhance software for state-of-the-art big data and ML applications.
Required Qualifications
- Bachelor's Degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing
- At least 4 years of experience programming with Python, Scala, or Java
- At least 3 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience leading teams developing ML solutions
Desired Qualifications
- Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
- 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or XGboost
- 3+ years of experience developing performant, resilient, and maintainable code
- 3+ years of experience with data gathering and preparation for ML models
- 3+ years of people management experience
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
- 3+ years of experience building production-ready data pipelines that feed ML models
- Ability to communicate complex technical concepts clearly to a variety of audiences
- Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion
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