Capital One

location-iconCapital One

Senior Machine Learning Engineer

location-iconPoplar Grove, IL, 61065

jobtype-iconPart Time, Full Time

estimated-salary-icon$63,602 per year

dateposted-iconPosted 8 days ago

Apply Now

location-iconActively Hiring

Center 1 (19052), United States of America, McLean, Virginia

Senior Machine Learning Engineer

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.

What you'll do in the role:
  • The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
  • 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 your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
  • 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 to create and enhance software that enables state-of-the-art big data and ML applications.
  • 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, including test automation and monitoring, to ensure successful deployment of ML models and application code.
  • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
  • 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
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, or another type of work authorization).

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

New York City (Hybrid On-Site): $165,100 - $188,500 for Senior Machine Learning Engineer

Sales Territory: $140,000 - $159,800 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.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days.

No agencies please. Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex (including pregnancy, childbirth or related medical conditions), race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, gender reassignment, citizenship, immigration status, protected veteran status, or any other basis prohibited under applicable federal, state or local law. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

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FAQ's

Find the answers for the most frequently asked questions below

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Engineering students typically learn subjects such as mathematics, physics, and computer science, as well as specialized engineering topics like mechanics, materials science, and engineering design, depending on their chosen field of study (e.g., electrical, mechanical, civil, computer, etc.). The goal is to equip them with the knowledge and skills necessary to design, build, and test engineering systems.

A Learning Engineer designs, develops, and maintains machine learning systems and applications. They often work on projects that involve data analysis, model building, and deployment, with a focus on making machine learning solutions scalable, efficient, and user-friendly.

Learning engineers typically study a combination of subjects in school, including: 1. Mathematics (Calculus, Linear Algebra, Statistics) 2. Computer Science (Programming, Data Structures, Algorithms) 3. Electrical Engineering (Circuits, Systems, Signals & Systems) 4. Machine Learning (Neural Networks, Reinforcement Learning, Deep Learning) 5. Software Engineering (Software Development, Design Patterns, Agile Methodologies) 6. Artificial Intelligence (AI Principles, Robotics, Natural Language Processing) 7. Data Science (Data Mining, Data Visualization, Machine Learning Algorithms) 8. Human-Computer Interaction (UX Design, Usability, Accessibility) These subjects provide a strong foundation for a learning engineer to develop, implement, and evaluate machine learning models and systems.

While a degree can be beneficial, it's not strictly necessary to become a Learning Engineer. Relevant experience, strong programming skills, and a solid understanding of machine learning principles are more important. Certifications and online courses can also demonstrate competency in the field. However, a degree can still provide additional advantages in the job market.

A learning engineer designs, develops, and implements machine learning models and systems to improve and optimize various processes, often in the context of education or training, but can also be applied to other fields such as data analysis or artificial intelligence.

Yes, a software engineer can transition to a machine learning engineer role by acquiring knowledge in areas such as statistics, data structures, algorithms, and machine learning principles. This often involves learning programming languages like Python and frameworks like TensorFlow or PyTorch.

While a master's degree can be beneficial for a Machine Learning Engineer role, it is not strictly necessary. Many Machine Learning Engineers have a background in computer science, mathematics, statistics, or a related field. Practical experience with machine learning algorithms, programming skills, and a strong understanding of data structures and algorithms are often more important than a master's degree. However, a master's degree can provide additional knowledge and specialization in the field.

Yes, a mechanical engineer can transition to a machine learning engineer. The transition requires learning programming, data structures, algorithms, and machine learning principles. However, understanding the mechanical aspects of systems can provide a unique advantage in applying machine learning to mechanical systems.

As a learning engineer, one learns about instructional design, educational technology, learning analytics, and adult learning principles to create, implement, and evaluate effective learning experiences in various educational or corporate training contexts.

A learning engineer is a professional who designs, develops, and implements educational technology solutions to enhance learning experiences and improve educational outcomes. They often work in educational institutions or learning technology companies, using their skills in instructional design, technology, and data analysis to create effective learning environments.