Senior Machine Learning Engineer, Shopping - Remote

Senior Machine Learning Engineer, Shopping - Remote

Interested in joining a dynamic remote first engineering team in a fast-paced environment full of greenfield problem-solving? Then Capital One Shopping might be the place for you. Join us in supporting a growth-stage line of business with a startup mindset as we build technology to save our customers money. 

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 contribute to researching our next generation of models and recommendation systems to deliver value to our customers. You will use tools like Python, Pytorch, Transformers, language models, and other statistical tools.

This is more than just a job; it‘s an opportunity to be part of a collaborative and forward-thinking community, where your contributions will make a significant impact in an ever-dynamic tech landscape. Join us as we push boundaries and redefine the future of our industry.

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. 

• Design and research new models using data scientist experience/expertise

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.

Remote (Regardless of Location): $144,200 - $164,600 for Senior Machine Learning Engineer

 

McLean, VA: $158,600 - $181,000 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 . 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).