Our Purpose

We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.

Title and Summary

Senior Software Engineer, Applied Machine Learning (Semantic Search, Natural Language Processing (NLP), Large Language Models) – Priceless Platform

Senior Software Engineer, Applied Machine Learning (Semantic Search, Natural Language Processing (NLP), Large Language Models) – Priceless Platform

Overview

If you have 4+ years industry experience working on Natural Language and text based Machine Learning Technologies, such as Semantic Search, Natural Language Processing, Vector Databases, Foundation Models, or Large Language Models, come talk to us.

The Priceless Platform is Mastercard’s premier AWS hosted global platform for our customers and partners. Created by a Silicon Valley startup that Mastercard acquired, we are experiencing significant growth, and we are working to scale our platform. We are looking for you to bring additional Semantic Search Natural Language Processing (NLP) powered capabilities into our platform.

Job Summary:

We are seeking an hands-on software engineer to join our team and drive development and deployment of Applied Machine Learning capabilities into our platform, such as Semantic Search, improved Recommendations, improved Text processing and Translations, and possibly a conversational interface. The successful candidate will apply ML based Semantic Search techniques, Natural Language Processing and Foundational models, large language models (LLMs), to build and deploy these in our platform.

Responsibilities:

1. Explore and apply techniques like Semantic Search, to improve and scale the search functionality in our platform, using technologies like Elastic, etc.
2. Design and implement a scalable text processing flow that improves and scales our text and image content processing workflows, using state-of-the-art NLP, Foundation or LLM models, such as GPT, Claude, Gemini, BERT, or other transformer-based architectures.
3. Prepare high-quality training data or apply retrieval augmentation models to enhance the performance and accuracy of the systems.
4. Fine-tune and customize the LLM models to adapt them to the specific domain requirements of our recommendation system.
5. Develop and integrate evaluation metrics to continuously monitor and improve the performance of the recommendation engine.
6. Optimize the recommendation system for low latency, high throughput, and efficient resource utilization.
7. Stay up-to-date with the latest advancements in ML/NLP/LLM research and incorporate relevant techniques and models into the recommendation engine.
8. Collaborate with cross-functional teams, including product managers and software engineers, to integrate the recommendation engine seamlessly into our website and applications.

Qualifications:

1. Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
2. 4+ years proven industry experience working with Semantic Search (Elastic), Natural Language Processing, large language models (LLMs), transformer architectures, and deep learning frameworks (e.g., TensorFlow, PyTorch).
3. Solid understanding of natural language processing (NLP) techniques, including text preprocessing, embeddings, and language models.
4. Experience with retrieval augmentation models and their application in recommendation systems or related domains.
5. Strong programming skills in Python and familiarity with relevant libraries and tools (e.g., Hugging Face, NLTK, scikit-learn).
6. Knowledge of cloud computing platforms (e.g., AWS, GCP) and experience deploying and scaling AI/LLM models.
7. Excellent problem-solving, analytical, and debugging skills.
8. Ability to work collaboratively in a team environment and communicate complex technical concepts effectively.
#AI2

Mastercard is an inclusive equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;

• Ensure the confidentiality and integrity of the information being accessed;

• Report any suspected information security violation or breach, and

• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary based on location, experience and other qualifications for the role and may be eligible for an annual bonus or commissions depending on the role. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance), flexible spending account and health savings account, paid leaves (including 16 weeks new parent leave, up to 20 paid days bereavement leave), 10 annual paid sick days, 10 or more annual paid vacation days based on level, 5 personal days, 10 annual paid U.S. observed holidays, 401k with a best-in-class company match, deferred compensation for eligible roles, fitness reimbursement or on-site fitness facilities, eligibility for tuition reimbursement, gender-inclusive benefits and many more.