Job Description
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms.
You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
- Inform ML infrastructure decisions using understanding of ML modeling techniques and issues.
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
- Collaborate as part of a cross-functional Agile team.
- Retrain, maintain, and monitor models in production.
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
- Construct optimized data pipelines to feed ML models.
- Leverage continuous integration and continuous deployment best practices.
- Ensure all code is well-managed to reduce vulnerabilities and models are well-governed from a risk perspective.
- Use programming languages like Python, Scala, or Java.
Basic Qualifications
- Bachelor’s degree
- At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)
- At least 3 years of experience designing and building data-intensive solutions using distributed computing
- At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
- At least 1 year of experience productionizing, monitoring, and maintaining models
Preferred Qualifications
- 1+ years of experience building, scaling, and optimizing ML systems
- 1+ years of experience with data gathering and preparation for ML models
- 2+ years of experience developing performant, resilient, and maintainable code
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
- 3+ years of experience with distributed file systems or multi-node database paradigms
- Contributed to open source ML software
- Authored/co-authored a paper on a ML technique, model, or proof of concept
- 3+ years of experience building production-ready data pipelines that feed ML models
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Salary Information
The minimum and maximum full-time annual salaries for this role are listed below, by location:
New York City (Hybrid On-Site): $165,100 - $188,500 for Senior Machine Learning Engineer
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
Benefits
This role is also eligible to earn performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
Application Information
This role is expected to accept applications for a minimum of 5 business days. No agencies please.
Equal Opportunity Employer
Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace.