• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development. • Lead collaborations and align across Personalization to integrate and A/B test mid-term signals in various recommendation systems. • Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization.

Sr. Director, Machine Learning Engineering (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: - Lead and scale a high-performing engineering organization responsible for the Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across Capital One products and services. - Define the technical strategy, delivery roadmap, and operating model for a portfolio spanning recommendation systems, ranking, decisioning, GenAI infrastructure, MLOps, and low-latency application-serving systems - Build, develop, and manage engineers and engineering leaders; set a high bar for hiring, performance, talent density, coaching, and succession planning across the organization - Partner cross-functionally with Product, Data Science, Cloud Infrastructure, and Machine Learning Platform teams to align strategy, prioritize investments, and co-develop advanced recommendation systems and algorithms serving Capital One users - Drive the design, buildout, and operation of robust ML infrastructure and pipelines supporting feature extraction, model training, testing, guardrails, evaluation, deployment, and both real-time and batch inference with strong reliability, scalability, and operational rigor - Architect low-latency, event-driven systems for real-time personalization and decisioning based on streaming data, user behavior, and contextual signals - Drive the evolution of MLOps practices through automated, metrics-backed deployment workflows, validation and testing systems, model lifecycle governance, and scalable observability - Guide the adoption of state-of-the-art AI and LLM optimization techniques to improve scalability, cost, latency, throughput, and reliability of large-scale production AI systems - Provide organizational technical and people leadership by influencing architecture, engineering standards, delivery excellence, incident management, and cross-team strategy while mentoring managers, tech leads, and senior engineers. - Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. - Attract and retain top talent in the AI industry and nurture personal and professional development for your team. Foster a culture of learning and staying abreast of the state-of-the-art in AI. Capital One is open to hiring a Remote Employee for this opportunity. Basic Qualifications: - Bachelor's degree in Computer Science, Engineering, or AI plus at least 10 years of experience developing or leading AI and ML algorithms or technologies, or Master's degree plus at least 8 years of experience developing or leading AI and ML algorithms or technologies - At least 5 years of people leadership experience Preferred Qualifications: - 7 years of experience managing and leading an engineering team - 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 Proven expertise designing, implementing, and scaling personalization platforms and recommendation systems across feed personalization, ads ranking, or targeted marketing messaging - Proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow) - Experience optimizing large-scale training and inference systems for hardware utilization, latency, throughput, and cost - Deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment Deep experience with MLOps, model observability, and production ML lifecycle management - Strong track record building organizations, developing managers and senior engineers, and leading through scale and ambiguity Excellent communication and presentation skills, with the ability to influence senior stakeholders and articulate complex AI concepts clearly - Proven leadership in driving platform strategy, cross-functional execution, and technical direction across a large organization - Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Remote (Regardless of Location): $286,200 - $326,700 for Sr. Dir, Machine Learning Engineering McLean, VA: $314,800 - $359,300 for Sr. Dir, Machine Learning Engineering Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. 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 . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. 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. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

• Implement, validate, and iterate on machine learning algorithms for weld perception tasks, including point cloud registration, seam detection, and joint geometry estimation, progressively expanding coverage across joint types and part geometries. • Build and maintain data pipelines for training and evaluating perception models, spanning annotated 3D scan data ingestion, synthetic data generation, and structured dataset management for iterative model improvement. • Run rigorous model evaluation experiments, including failure mode analysis, FP/FN rate characterization, and benchmarking against quantitative registration accuracy thresholds, and communicate findings clearly to guide next steps. • Integrate trained models into production ROS-based robotics services, ensuring low-latency inference and compatibility with deployed cell configurations. • Write clean, well-tested Python code; participate actively in code and experiment reviews; and maintain clear documentation of methods, parameters, and results. • Lead research, development, and production deployment of advanced perception algorithms spanning point cloud registration, seam detection, and real-time in-process tracking across structured light, RGB, and stereo sensors. • Design and lead experiments evaluating state-of-the-art deep learning models, including transformer-based and geometric feature learning architectures. • Design and lead real-time perception systems such as during-weld seam tracking, applying sensor fusion with probabilistic state estimation (e.g., Kalman filtering) to achieve robust weld performance. • Define and own the end-to-end ML lifecycle, from dataset design and annotation strategy through training, benchmarking, and fleet deployment, with clear go/no-go evaluation frameworks. • Architect distributed training and hyperparameter optimization workflows; drive strategy for data acquisition, annotation tooling, and synthetic vs. real scan data usage. • Mentor engineers across levels, providing technical leadership on perception systems and ML methodology.

Role Description We're looking for a Machine Learning Engineer (L5) to design and scale the next generation of intelligent systems that power data-driven decision-making across Block—from Cash App and Square to Corporate domains like Treasury, Cost, and Accounting. You'll build models and AI-driven workflows that don't just predict outcomes—they help shape them. Working across the full ML lifecycle, you'll transform raw data into foresight through advanced modeling, agentic AI workflows, and automation frameworks that enable faster, smarter decisions at scale. You'll partner closely with analytics and data science teams to bring experimental models into production and with finance and operations partners to build explainable, self-optimizing systems that make forecasts and insights transparent, actionable, and continuously learning. Qualifications - 5+ years of experience in software or ML engineering, with hands-on experience delivering production-grade ML systems. - Deep understanding of applied ML and forecasting, including time-series, regression, and value prediction modeling. - Strong proficiency in Python and common ML libraries such as scikit-learn, XGBoost, LightGBM, and NumPy/pandas. - Experience building data pipelines using tools such as Airflow, Spark, or similar orchestration systems, and working with BigQuery or other large-scale data warehouses. - Familiarity with model explainability techniques (e.g., SHAP, feature attribution, uncertainty quantification). - Excellent analytical and communication skills; able to connect model design to business objectives. - Proven ability to work cross-functionally and drive high-impact results in fast-paced environments. - Experience in forecasting or planning models in fintech, consumer, or marketplace settings. - Exposure to automated model serving, monitoring, or feedback loops in production. - Background in statistical modeling, uncertainty estimation, or model interpretability research. Requirements - Design and implement forecasting, financial, or optimization models that power strategic decisions across Block. - Build end-to-end ML pipelines for training, deployment, and monitoring, ensuring reproducibility and performance at scale. - Collaborate with Data Science to productionize experimental models and integrate them into live systems. - Partner with Analytics & Finance teams to ensure forecasts are interpretable, accurate, and aligned with business objectives. - Develop or contribute to explainability tools that communicate model drivers, confidence, and uncertainty to stakeholders. - Improve data pipelines and workflows using systems like Airflow, BigQuery, and Spark. - Establish and document best practices for model evaluation, experimentation, and maintenance. - Translate complex technical findings into clear, actionable recommendations for non-technical partners. - Contribute to a culture of curiosity, high-quality engineering, and continuous learning within the AIM organization. Benefits - Remote work - Medical insurance - Flexible time off - Retirement savings plans - Modern family planning