Job Title:
Principal Software Engineer, Applied Machine Learning (Semantic Search, Natural Language Processing (NLP), Large Language Models) – Priceless Platform
Overview:
Principal Software Engineer, Applied Machine Learning (Semantic Search, Natural Language Processing (NLP), Large Language Models) – Priceless Platform
Overview
If you have 8+ 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 experienced and hands-on senior 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. 8+ years proven industry experience working with Semantic Search (Elastic), 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.
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Location: San Francisco, CA (+1 other)
Posted: Sept. 16, 2024, 7:32 a.m.
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