The ideal candidate will be eager to quickly implement proofs of concept (PoCs) and contribute to taking them into production in an environment where innovation is key, and the systems are still evolving.

Responsibilities:

1. Contribute to rapidly prototyping PoCs and production-level code that implements solutions at scale. When appropriate, prioritize simple, effective methods to address business needs and iteratively build from there.

2. Explore and apply ML techniques like Semantic Search, to improve and scale the search functionality in our platform, using Elasticsearch, Vector Databases etc.

3. Design and implement scalable text and image content processing workflows, leveraging state-of-the-art NLP, Foundation Models, or LLM architectures (e.g., GPT, BERT, BART, MoE).

4. Contribute to preparing training data and assist in fine-tuning LLMs and retrieval augmentation models for specific domain requirements.

5. Assist in designing and implementing Recommendation Systems, including developing and applying evaluation metrics to monitor, assess, and iteratively improve the performance of models.

6. Support efforts to optimize the performance of our models and recommendation systems for low latency, high throughput, and efficient resource usage.

7. Stay up to date with the latest advancements in ML/NLP/LLM research and incorporate relevant techniques to improve the functionality of the platform.

8. Collaborate with cross-functional teams, including product managers and software engineers, to integrate the recommendation engine seamlessly into our website and applications.

Qualifications:

Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.

1-3 years of experience in software engineering or applied machine learning, ideally working with semantic search, LLMs, and transformer-based architectures.

Solid understanding of text preprocessing, embeddings, and language models, with experience or interest in applying them to real-world problems.

Exposure to frameworks like Langchain, Hugging Face, and other LLM-related libraries for model deployment, fine-tuning, or agent application development is a plus.

Strong programming skills in Python, including familiarity with libraries and tools such as scikit-learn, NLTK, PyTorch, TensorFlow, and Hugging Face.

Experience with cloud platforms (e.g., AWS, GCP) and deploying machine learning models at scale is a plus.

Excellent problem-solving, analytical, and debugging skills with a willingness to work in an environment where you're building foundational systems, rapidly prototyping solutions, and iterating toward production.

Ability to work collaboratively in a team environment and communicate complex technical concepts effectively.

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.

Pay Ranges
San Francisco, California: $138,000 - $221,000 USD