S'INSCRIRE

Offres d'emploi

Trouvez des postes près de chez vous, sur site, hybrides ou à distance.
  • Emplois similaires à : Machine Learning Engineer / Data Scientist
XX
Sr. Distinguished Machine Learning Engineer (Remote-Eligible)Capital One • Richmond, Virginia, United States
Postuler Maintenant
XX

Sr. Distinguished Machine Learning Engineer (Remote-Eligible)

Capital One
  • US
    Richmond, Virginia, United States
  • US
    Richmond, Virginia, United States
Postuler Maintenant

À propos

Sr. Distinguished Machine Learning Engineer (Remote-Eligible)
Overview At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.
Team Description The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One’s consumer products and organizations, by providing well-managed, self-service, experimentation-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real-time customer experiences at scale — turning every channel into a context-aware decisioning surface, from home feeds to marketing and servicing messages. The org’s mission is to move Capital One to deliver always-on, cohort-of-one personalization, powered by resilient data foundations, production-grade ML and GenAI systems, and low-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment.
What you’ll do in the role
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.
The Ideal Candidate
You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good.
Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production.
You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven.
You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss.
You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown.
Basic Qualifications
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
Preferred Qualifications
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.
Passion for staying on top of the latest AI research and AI systems, and judiciously apply novel techniques in production
Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
Proven leadership in driving platform strategy, fostering cross‑functional collaboration, and influencing technical direction across the company.
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Capital One is open to hiring a Remote Employee for this opportunity.
Remote (Regardless of Location): $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer
McLean, VA: $314,800 - $359,300 for Sr Distinguished Machine Learning Engineer
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 (https://www.capitalonecareers.com/benefits). Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
#J-18808-Ljbffr
  • Richmond, Virginia, United States

Compétences linguistiques

  • English
Avis aux utilisateurs

Cette offre provient d’une plateforme partenaire de TieTalent. Cliquez sur « Postuler maintenant » pour soumettre votre candidature directement sur leur site.