Senior Machine Learning Engineer

Stelle nicht auf LinkedIn

🕒 vor 4 Monaten

⚔️ Virginia – Remote

⚔️ Virginia – Remote

💵 $286.200 - $326.700 / Jahr

⏰ Vollzeit

🟠 Senior

🤖 Machine-Learning-Entwickler

🦅 H1B-Visum-Sponsor

Dieses Unternehmen hat in der Vergangenheit H1B-Visa gesponsert.

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Capital One

WebsiteLinkedInAlle Stellen

10.000+ Mitarbeiter

🏦 Bankwesen

💳 Fintech

💸 Finanzen

💰 Post-IPO Equity im 2023-05

Banking • Fintech • Finance

Capital One ist ein Finanzdienstleistungsunternehmen, das sich auf Kreditkarten, Banken und Kredite spezialisiert hat. Das Unternehmen ist dem Innovationsgedanken verpflichtet und kombiniert Technologie- und datengesteuerte Strategien, um das Kundenerlebnis zu verbessern und seine Bankdienstleistungen zu optimieren. Capital One legt zudem großen Wert auf Vielfalt, Inklusion und eine kooperative Arbeitskultur und bietet eine Vielzahl von Arbeitsmöglichkeiten in verschiedenen Bereichen, darunter Finanzen, Technologie und Kundenservice.

Beschreibung

• 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.

🎯 Anforderungen

• 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

🏖️ Vorteile

• 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)