Senior Machine Learning Engineer

🕒 April 7

⚔️ Virginia – Remote

⚔️ Virginia – Remote

💵 $286.2k - $326.7k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Capital One

WebsiteLinkedInAll Job Openings

10,000+ employees

🏦 Banking

💳 Fintech

💸 Finance

💰 Post-IPO Equity on 2023-05

Banking • Fintech • Finance

Capital One is a leading financial services company that specializes in offering credit cards, auto loans, banking, and savings accounts. With a focus on innovation and technology, Capital One aims to change banking for good by providing customer-friendly solutions and fostering a diverse and inclusive workforce. The company is known for its commitment to creating a positive impact in the banking industry through advanced digital tools and customer service excellence.

📋 Description

• Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services. • Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users. • Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations. • Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability. • Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals. • Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability. • Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems. • Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. • Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation.

🎯 Requirements

• Bachelor’s degree • At least 10 years of experience designing and building data-intensive solutions using distributed computing • At least 7 years of experience programming in C, C++, Python, or Scala • At least 4 years of experience with the full ML development lifecycle using modern technology in a business critical setting • 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure); Master's or PhD in Computer Science or a relevant technical field. • 5+ years of proven expertise in designing, implementing and scaling personalization platform and recommendation systems serving one or more areas of Feed Personalization/Ads Ranking/Targeted Marketing Messaging. • 5+ years of strong proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow). • 5+ years of experience developing and applying state-of-the-art techniques for optimizing training and inference systems to improve hardware utilization, latency, throughput, and cost. • 5+ years of deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment. • Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers

🏖️ Benefits

• Comprehensive, competitive, and inclusive set of health, financial and other benefits • Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)