Capital One
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Bengaluru · On-site · 3-6 Yrs · Oct 27
Banking & Lending · 10000+ Employees · Mc Lean, US
Capital One is passionate about building a culture that benefits all associates. Here, people from different backgrounds, experiences and education levels come together to create a sense of belonging. Everyone is supported in the work they want to do and in getting where they want to go. A career here is an opportunity to create work that matters—while maintaining a healthy work/life balance. We’re connecting customers to the tools and resources that will improve their financial lives. What’s in it for you? A career surrounded by brilliant people and supported by great benefits and wellbeing. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Hiring: Engineering
What employees say
+ great culture and nice team
− india team doesnt get great work
More reviews on the company profile.
Interview experience · Associate
The interview were in 4 phases, and questions asked were relevant to the job role. First round is screening where they talk more about the company than your skills. Then second and third round are more focused on the JD.
Average difficulty · Bengaluru · 129 reports
More interview insightsJob description
Overview
Voyager (94001), India, Bangalore, Karnātaka
Responsibilities
- 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
Qualifications
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics or 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 like Pandas, NumPy and Scikit-learn
- At least 6 years of experience using and operating large scale distributed systems such as Spark and 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 such as AWS, GCP, or Azure and using Kubernetes to manage large scale containerized Machine Learning software systems
- 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 including Supervised, semi-supervised, unsupervised, and reinforcement learning, model types including Regression, Classification, and Clustering, model Architectures including RNNs, CNNs, LSTMs and Transformers, training concepts such as loss function, hyperparameters, and regularization, and how to evaluate model accuracy and diagnose and address common issues such as underfitting and overfitting
- 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
- Ability to communicate complex technical concepts clearly to a variety of audiences
- Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents
- Experience hiring and developing high-performing ML engineers with an inspiring leadership style
- Highly developed interpersonal, presentation, and communications skills
- Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents
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Job application FAQ
Last updated 2 November 2026
- How do I apply for Director - Machine Learning Engineering at Capital One?
- Open this job on Feedback AI and sign up free (or sign in). You can apply from the job detail page; match scores and skill gaps appear when your profile is complete. Capital One may attach structured assessments to interview rounds after you apply.
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- Is Director - Machine Learning Engineering remote or on-site in Bengaluru, Karnataka?
- This listing is marked on_site with location Bengaluru, Karnataka. Confirm details on the job description before applying.
- What skills matter for this Director - Machine Learning Engineering role at Capital One?
- Key skills for this role include skills listed in the job description. Upload a resume to see which requirements you already meet and which are gaps.