Lead Machine Learning Engineer (Manager IC)
$197,300–$225,100 year
On-siteMcLean, Virginia, United States
EXPIREDMcLean, Virginia, United StatesOn-siteFull Time$197,300–$225,100 yearSenior LevelBachelors DegreeEnterprise
Full TimeSenior LevelBachelors DegreeEnterprise
Job Summary
Design, build, and deliver ML models and components that solve real-world business problems, including fraud, marketing, and servicing applications. Lead technical design for self-supervised learning on transformer architectures, driving model architecture decisions and ensuring high availability in production. Collaborate with cross-functional Agile teams to develop optimized data pipelines, automate testing and deployment, and maintain model performance. Implement responsible AI practices, secure code, and leverage cloud architectures to scale solutions using Python, Scala, or Java.
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
- At least 2 years of experience building, scaling, and optimizing ML systems
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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