Sr. Director, Machine Learning Engineering (Remote-Eligible) locations McLean, VA US Remote time type Full time job requisition id R239903 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

We’re looking for a Machine Learning Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale. Who We Are At Seedtag, our mission is to transform advertising by proving that effectiveness and user privacy can truly coexist. As the leading Neuro-Contextual Advertising Company, we combine Artificial Intelligence, Natural Language Processing, Computer Vision, and neuroscience to understand not only what content is about, but how it makes people feel and what they intend to do next. Our proprietary AI, Liz, enables brands to connect with audiences across the open web and Connected TV without cookies or user tracking. Founded in 2014 by two ex-Googlers, Seedtag has grown to 700+ Seedtaggers in 17 countries, backed by €250M in funding, and operates today as a global ad-tech leader. If you enjoy solving complex engineering challenges and building AI-driven systems at scale, you’ll feel right at home here. Your Challenge - As a Machine Learning Engineer on Seedtag's Ad Exchange team, you will: - Build cutting-edge AI to optimise revenue flow while ensuring the needs of publishers and advertisers are met. - Research, design, test, deploy, and maintain AI models in a fully online environment to maximise margins, reduce operational costs, and enhance Seedtag's targeting capabilities. - Design and implement classical ML algorithms and control systems to ensure delivery of internal campaigns and maximise monetisation outcomes. - Build end-to-end data pipelines to train, validate, and analyse production model behaviour through custom dashboards. - Continuously improve our MLOps infrastructure, CI/CD pipelines, internal automations, and AI-supported workflows. - Collaborate closely with Data, Platform, and Backend Engineers to build services and infrastructure, from dataset generation to live model validation. Our Core Values Outcome over Output We measure success by impact and value, not by volume of features or lines of code. Failure Is Allowed, Learning Is a Must Experimentation is key to innovation. We test early, iterate often, and learn fast. We Are All Scouts We take ownership and leave things better than we found them. We Are Data-Driven Data informs our decisions and helps us continuously improve our systems and results. Tech Stack We operate at a large scale, supporting up to 120k requests per second, with ML models responding in under 10 milliseconds and processing 20 TB of data daily. Our stack includes: - Python & Go microservices - Kafka, Kinesis, Redis, GCS - Kubernetes on GCP & AWS - Druid, MongoDB, scalable data lake architecture - Typescript (Node.js) and Scala across other parts of the company What You’ll Need to Succeed - 3–6 years of experience building and deploying ML systems in production. - Strong Python skills and solid software engineering fundamentals (APIs, async programming, testing, clean architecture). - Experience working on both model development and production deployment. - Understanding of distributed systems, microservices, and cloud-native environments. - Familiarity with MLOps practices: model versioning, monitoring, CI/CD, reproducibility. - Experience with NLP, embeddings, and/or ranking models is a plus. - Comfortable debugging across layers: model behaviour, data issues, API performance, infrastructure bottlenecks. - Strong ownership mindset and ability to operate autonomously in fast-moving environments. Why Join Seedtag? - A key moment of growth with real ownership and global impact. - Flexible work model with 100% remote or hybrid options. (Remote contracts available in Spain, Italy, the UK, Belgium, the Netherlands, France, and Germany.) - Continuous learning through a learning platform and optional language classes. - A supportive, trust-based culture that values well-being. - Team activities, offsites, and opportunities to connect beyond work. Additional Perks - Home office setup budget up to €1,000 - Paid trips to our HQ in Madrid - MacBook Pro M3 Ready to Join the Seedtag Adventure? At Seedtag, we create an environment where everyone can thrive. If you need accommodations during the hiring process, let us know, and we’ll ensure a positive experience. Send us your CV and let’s build the future of neuro-contextual advertising together.

We’re looking for a Machine Learning Engineer to join our tech team and help build the future of neuro-contextual advertising at global scale. Who We Are At Seedtag, our mission is to transform advertising by proving that effectiveness and user privacy can truly coexist. As the leading Neuro-Contextual Advertising Company, we combine Artificial Intelligence, Natural Language Processing, Computer Vision, and neuroscience to understand not only what content is about, but how it makes people feel and what they intend to do next. Our proprietary AI, Liz, enables brands to connect with audiences across the open web and Connected TV without cookies or user tracking. Founded in 2014 by two ex-Googlers, Seedtag has grown to 700+ Seedtaggers in 17 countries, backed by €250M in funding, and operates today as a global ad-tech leader. If you enjoy solving complex engineering challenges and building AI-driven systems at scale, you’ll feel right at home here. Your Challenge As a Machine Learning Engineer on Seedtag's Ad Exchange team, you will: - Build cutting-edge AI to optimise revenue flow while ensuring the needs of publishers and advertisers are met. - Research, design, test, deploy, and maintain AI models in a fully online environment to maximise margins, reduce operational costs, and enhance Seedtag's targeting capabilities. - Design and implement classical ML algorithms and control systems to ensure delivery of internal campaigns and maximise monetisation outcomes. - Build end-to-end data pipelines to train, validate, and analyse production model behaviour through custom dashboards. - Continuously improve our MLOps infrastructure, CI/CD pipelines, internal automations, and AI-supported workflows. - Collaborate closely with Data, Platform, and Backend Engineers to build services and infrastructure, from dataset generation to live model validation. Our Core Values Outcome over Output We measure success by impact and value, not by volume of features or lines of code. Failure Is Allowed, Learning Is a Must Experimentation is key to innovation. We test early, iterate often, and learn fast. We Are All Scouts We take ownership and leave things better than we found them. We Are Data-Driven Data informs our decisions and helps us continuously improve our systems and results. Tech Stack We operate at a large scale, supporting up to 120k requests per second, with ML models responding in under 10 milliseconds and processing 20 TB of data daily. Our stack includes: - Python & Go microservices - Kafka, Kinesis, Redis, GCS - Kubernetes on GCP & AWS - Druid, MongoDB, scalable data lake architecture - Typescript (Node.js) and Scala across other parts of the company What You’ll Need to Succeed - 2–4 years of experience building and deploying ML systems in production. - Strong Python skills and solid software engineering fundamentals (APIs, async programming, testing, clean architecture). - Experience working on both model development and production deployment. - Understanding of distributed systems, microservices, and cloud-native environments. - Familiarity with MLOps practices: model versioning, monitoring, CI/CD, reproducibility. - Experience with NLP, embeddings, and/or ranking models is a plus. - Comfortable debugging across layers: model behaviour, data issues, API performance, infrastructure bottlenecks. - Strong ownership mindset and ability to operate autonomously in fast-moving environments. Why Join Seedtag? - A key moment of growth with real ownership and global impact. - Flexible work model with 100% remote or hybrid options. (Remote contracts available in Spain, Italy, the UK, Belgium, the Netherlands, and Germany.) - Continuous learning through a learning platform and optional language classes. - A supportive, trust-based culture that values well-being. - Team activities, offsites, and opportunities to connect beyond work. Additional Perks - Home office setup budget up to €1,000 - Paid trips to our HQ in Madrid - MacBook Pro M3 Ready to Join the Seedtag Adventure? At Seedtag, we create an environment where everyone can thrive. If you need accommodations during the hiring process, let us know and we’ll ensure a positive experience. Send us your CV and let’s build the future of neuro-contextual advertising together.

• Implementation of production ML infrastructure: Design, implement, and maintain machine learning pipelines and systems in production environments, ensuring scalability, reliability, monitoring, and integration with the platform architecture • Development and evolution of product-focused models: Develop, train, validate, and optimize ML models with an emphasis on direct impact to product features and value generation for customers • MLOps and model governance: Establish practices for model versioning, deployment, monitoring, auditing, and traceability, ensuring reproducibility, security, and compliance across the solution lifecycle • Monitoring and performance of ML systems: Track performance, accuracy, drift, and model stability metrics in production, proposing continuous adjustments to ensure operational efficiency and sustainability • Integration with engineering architecture: Collaborate with Software Engineering and Data Engineering to ensure ML systems align with architectural standards, coding best practices, and scalability requirements • Code quality and sustainability: Produce clean, testable, and efficient code, considering time complexity, computational cost, and long-term maintainability, following mature engineering standards • Applied research and product-driven innovation: Explore new techniques, frameworks, and ML approaches with a focus on practical applicability and concrete improvements to product and infrastructure • Cross-functional collaboration and technical support: Act as a technical ML reference within Engineering, supporting Product and other areas in understanding intelligent systems and ensuring alignment between strategy and technical execution