Lead Machine Learning Engineer (Gen AI, Python, Go, AWS)

As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You’ll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You’ll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and components that solve complex business business problems, while working in collaboration with the Product and Data Science teams.
• Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models.
• Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment.
• Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications.
• Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints.
• Retrain, maintain, and monitor models in production.
• Construct optimized, scalable data pipelines to feed ML models.
• 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, Go, Scala or Java

Basic Qualifications:

• Bachelor’s Degree
• At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
• At least 4 years of experience programming with Python, Scala, Go or Java
• At least 2 years of experience building, scaling, and optimizing ML systems

Preferred 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

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, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).

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.

• Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer
• McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer
• New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer
• San Francisco, CA: $215,200 - $245,600 for Lead 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 Capital One Careers website . 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.