About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.
Job Description
Senior Machine Learning Engineer – Merchant Data Platform:
We are looking for a Senior Machine Learning Engineer to join our Merchant Data Platform (MDP) AI/ML team, helping to build the next generation of an AI-powered merchant ecosystem.
In this role, you will leverage cutting-edge machine learning and generative AI techniques to enhance merchant data, improve data quality, and unlock actionable insights that power critical business decisions across Visa’s global ecosystem.
Key Responsibilities:
- Design, develop, and deploy scalable machine learning models and pipelines to enrich merchant data, including entity resolution, attribute inference, and data standardization
- Build end-to-end ML solutions (data ingestion → feature engineering → model training → deployment → monitoring), ensuring high performance, reliability, and scalability
- Apply advanced techniques such as NLP, LLMs, and probabilistic modeling to solve challenges like merchant name normalization, brand hierarchy mapping, and data deduplication
- Partner closely with product managers, data engineers, and platform teams to translate business problems into ML-driven solutions and influence product direction
- Develop and maintain data quality frameworks and observability systems to continuously monitor model performance, detect drift, and improve accuracy
- Optimize models and systems for latency, throughput, and cost efficiency, especially in real-time and large-scale environments
- Contribute to and improve MLOps practices, including CI/CD pipelines, feature stores, model lifecycle management, and experimentation frameworks
- Mentor junior engineers, review designs/code, and help elevate the team’s technical standards and best practices
What We’re Looking For:
- Strong experience building and deploying production-grade machine learning systems at scale
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or similar
- Hands-on experience with distributed systems, big data technologies (e.g., Spark), and cloud platforms.
- Solid understanding of ML fundamentals, including model evaluation, feature engineering, and data pipelines.
- Experience with real-time inference systems, data pipelines, and MLOps tooling
- Strong problem-solving skills with the ability to handle ambiguous, open-ended problems independently.
- Excellent communication and collaboration skills, with the ability to work effectively across teams
Impact You’ll Make:
- Enable high-quality, standardized merchant data across global markets
- Unlock new insights and data products that drive business value and innovation
- Help scale Visa’s AI/ML capabilities across multiple regions and use cases
- Drive technical excellence and best practices in machine learning engineering.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience.
- Experience in developing and implementing AI/ML models and algorithms.
- Experience in designing and building scalable machine learning pipelines.
Preferred Qualifications:
- 3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD).
- Experience in collaborating with cross-functional teams to integrate AI/ML solutions.
- Experience in collecting, preprocessing, and analyzing large datasets.
- Experience in training and evaluating machine learning models.
- Experience in modernizing legacy code and adopting emerging technologies.
- Experience in acting as a design authority and shaping best practices within engineering teams.
- Experience in communicating technical concepts to non-technical stakeholders.
- Experience in leading multiple workstreams in AI application development.
- Experience in generative AI and large language models (LLMs).
- Experience in infrastructure automation development and enhancing productivity using LLM models.
- Experience in developing robust and scalable products for cybersecurity.
- Experience in conducting research and experimenting with new AI/ML techniques.
- Experience in mentoring junior team members and leading implementations on key modules.
U.S. Applicants Only
The estimated salary range for this position is $145,200.00 to $ 224,800.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.
Work Hours
Varies upon the needs of the department.
Travel Requirements
This position requires travel 5-10% of the time.
Mental/Physical Requirements
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.
Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
