Machine Learning Engineer - Bank Technology

About

We are Capital One, seeking a Distinguished Machine Learning Engineer to collaborate with our fraud, risk, data, and platform teams. Our mission is to advance the bank's proactive fraud prevention strategy through generative AI technologies. This role emphasizes responsible application of GenAI capabilities, transitioning our approach from reactive detection to proactive, intelligence‑driven fraud prevention. We offer a dynamic work environment with a commitment to professional development, and we provide a competitive benefits package to support your overall well‑being. Join us in pushing the boundaries of technology in the financial services industry.

Requirements

Must have:

• Bachelors degree in a related field
• A minimum of 10 years of experience in designing and developing data-driven solutions with distributed computing
• At least 6 years of programming experience in C, C++, Python, or Scala
• A minimum of 3 years of experience with the complete machine learning development lifecycle in a critical business environment
• Masters degree preferred
• 3+ years of experience in designing and implementing scalable production-quality data pipelines for machine learning models
• 2+ years of experience with Dask, RAPIDS, or in High‑Performance Computing
• 2+ years of experience utilizing the PyData ecosystem (NumPy, Pandas, and Scikit‑learn)
• Familiarity with various AI technology aspects, including prompt engineering, guardrails, vector databases, LLM fine‑tuning, and evaluation
• Strong ability to articulate complex technical concepts to diverse audiences
• Evidence of industry impact through presentations, publications, blogs, or open‑source contributions
• Capacity to attract and cultivate skilled software engineers through effective leadership

Responsibilities

• Deliver machine learning models and software that resolve complex business challenges within the financial sector, collaborating closely with Product, Architecture, Engineering, and Data Science teams
• Offer expertise in assessing and adopting generative AI methods and tools such as LangChain, LangGraph, LLMs, RAG, MCP, embeddings, vector stores, and model orchestration frameworks
• Spearhead the development and enhancement of machine learning models and software that support cutting‑edge intelligent systems
• Lead extensive machine learning projects focusing on customer needs
• Facilitate the adoption of established generative AI methodologies across teams by shaping platform design and technical strategies
• Utilize cloud infrastructure and technologies to deploy optimized machine learning models at scale
• Enhance data pipelines that feed machine learning models
• Apply programming languages like Python, Scala, or C/C++
• Use compute technologies such as Dask and RAPIDS effectively
• Advocate for best practices across all dimensions of the engineering and modeling lifecycles
• Assist in attracting, developing, and retaining top engineering talent