Principal Software Engineer, Applied Machine Learning
Job location
San Francisco/O Fallon, United States of America
Type
Full Time
Responsibilities:
Explore and apply techniques like Semantic Search, to improve and scale the search functionality in our platform, using technologies like Elastic, etc.
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.
Prepare high-quality training data or apply retrieval augmentation models to enhance the performance and accuracy of the systems.
Fine-tune and customize the LLM models to adapt them to the specific domain requirements of our recommendation system.
Develop and integrate evaluation metrics to continuously monitor and improve the performance of the recommendation engine.
Optimize the recommendation system for low latency, high throughput, and efficient resource utilization.
Stay up-to-date with the latest advancements in ML/NLP/LLM research and incorporate relevant techniques and models into the recommendation engine.
Collaborate with cross-functional teams, including product managers and software engineers, to integrate the recommendation engine seamlessly into our website and applications.
Requirements & Skills:
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
8+ years of proven industry experience working with Semantic Search (Elastic), large language models (LLMs), transformer architectures, and deep learning frameworks (e.g., TensorFlow, PyTorch).
Solid understanding of natural language processing (NLP) techniques, including text preprocessing, embeddings, and language models.
Experience with retrieval augmentation models and their application in recommendation systems or related domains.
Strong programming skills in Python and familiarity with relevant libraries and tools (e.g., Hugging Face, NLTK, sci-kit-learn).
Knowledge of cloud computing platforms (e.g., AWS, GCP) and experience deploying and scaling AI/LLM models.
Excellent problem-solving, analytical, and debugging skills.
Ability to work collaboratively in a team environment and communicate complex technical concepts
