Jobs / United States / Capital One Services LLC
Director, Machine Learning Engineer
Capital One Services LLC · 🇺🇸 McLean, VA
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 838 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 253 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Capital One Services LLC paid sponsored hires in similar roles2 certified filings for “Senior AI Engineer” (Computer and Information Research Scientists) in VA: $137k–$162k, median $149k. Most were filed at wage level I (50%) — 1 lottery entry, ≈15% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Capital One Services LLC
The US Department of Labor certified 838 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 253 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Capital One Services LLC →
About the role
Director, Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You’ll Do: • Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams • Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems • Lead large-scale ML initiatives with the customer in mind • Leverage cloud-based architectures and technologies to deliver optimized ML models at scale • Optimize data pipelines to feed ML models • Use programming languages like Python, Scala, or Java • Evangelize best practices in all aspects of the engineering and modeling lifecycles • Recruit, nurture, and retain top engineering talent • Serve as a force-multiplier for the team, balancing deep, hands-on technical contribution and innovation with mentoring and elevating the skills of peers and junior engineers • Recruit, nurture, and retain top engineering talent • Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community Basic Qualifications: • Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) • At least 3 years of people leadership experience • At least 8 years of experience programming with Python, Java, Golang, or C++ • At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) • At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data • At least 5 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: • Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field • 5+ years of experience managing and leading an engineering team • 3+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference • 5+ years of experience optimizing ML algorithms, configurations, and infrastructure • 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) • 7+ years of experience designing, implementing, and scaling production-ready data pipelines for t