Distinguished Machine Learning Engineer - Bank Tech

Capital One•McLean, VA

16d•$244,700 - $335,100

About The Position

Distinguished Machine Learning Engineer - Bank Tech Overview: As a Capital One Machine Learning Engineer, you'll be providing technical leadership to engineering teams 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 serve as a technical domain expert in machine learning, guiding machine learning architectural design decisions, developing and reviewing model and application code, and ensuring 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. You’ll also mentor other engineers and further develop your technical knowledge and skills to keep Capital One at the cutting edge of technology. Team Description We are seeking a Distinguished Machine Learning Engineer to partner with fraud, risk, data, and platform teams to advance the bank’s proactive fraud prevention strategy, leveraging generative AI technologies to anticipate emerging threats, accelerate insight generation, and enhance decisioning at scale. This role will help shape how GenAI capabilities are responsibly applied across the organization to move from reactive detection to proactive, intelligence-driven fraud prevention. What you’ll do in the role: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Provide guidance on the evaluation and adoption of generative AI techniques and tools, including LangChain, LangGraph, LLMs, RAG, MCP, embeddings, vector stores, and model orchestration frameworks. Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Drive adoption of proven generative AI patterns across teams by influencing platform design, standards, and technical direction. Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, C/C++ Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent

Requirements

Bachelor’s degree
At least 10 years of experience designing and building data-intensive solutions using distributed computing
At least 6 years of experience programming in C, C++, Python, or Scala
At least 3 years of experience with the full ML development lifecycle using modern technology in a business critical setting

Nice To Haves

Master's Degree
3+ years of experience designing, implementing, and scaling production-ready data pipelines that feed ML models
2+ years of experience using Dask, RAPIDS, or in High Performance Computing
2+ years of experience with the PyData ecosystem (NumPy, Pandas, and Scikit-learn)
Experience with one or multiple areas of AI technology stack including prompt engineering, guardrails, vector databases/knowledge bases, LLM fine-tuning, LLM Evaluation.
Ability to communicate complex technical concepts clearly to a variety of audiences
ML industry impact through conference presentations, papers, blog posts, or open source contributions
Ability to attract and develop high-performing software engineers with an inspiring leadership style

Responsibilities

Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams
Provide guidance on the evaluation and adoption of generative AI techniques and tools, including LangChain, LangGraph, LLMs, RAG, MCP, embeddings, vector stores, and model orchestration frameworks.
Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems
Lead large-scale ML initiatives with the customer in mind
Drive adoption of proven generative AI patterns across teams by influencing platform design, standards, and technical direction.
Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
Optimize data pipelines to feed ML models
Use programming languages like Python, Scala, C/C++
Leverage compute technologies such as Dask and RAPIDS
Evangelize best practices in all aspects of the engineering and modeling lifecycles
Help recruit, nurture, and retain top engineering talent