Machine Learning Engineer - Foundation Models & Advanced NLP

Location: San Jose, CA (Hybrid)

About Us: We are an innovative technology company committed to building intelligent systems that streamline complex data-driven processes. We leverage cutting-edge machine learning and natural language processing (NLP) techniques to provide deeper insights and enhance decision-making across various domains.

Role Overview: We are looking for an experienced Machine Learning Engineer with expertise in foundation models, large language models (LLMs), retrieval-augmented generation (RAG), and advanced NLP methodologies. In this role, you will design and implement AI-driven systems that can effectively process, evaluate, and summarize large collections of text data to support our internal analytics and decision-making processes. Your work will be central to building models capable of understanding and reasoning about vast amounts of unstructured data, ranking and scoring them based on predefined criteria, and generating insightful summaries.

Key Responsibilities:

• Model Development: Design, train, and optimize machine learning models, with a focus on foundation models and LLMs, for processing and reasoning over large datasets.

• Information Retrieval and RAG: Develop and implement retrieval-augmented generation systems to enhance model performance on specific, information-heavy tasks.

• Data Understanding and Analysis: Build models to interpret, rank, and summarize data, particularly unstructured text data, in a way that supports streamlined analysis and intelligent decision-making.

• Feature Engineering: Collaborate across the board to extract meaningful features, construct embeddings, and refine data pipelines that improve model outcomes.

• Evaluation and Fine-Tuning: Continuously evaluate and fine-tune models for accuracy, performance, and relevance to evolving business needs.

• Cross-Functional Collaboration: Work closely with cross-functional teams, including data science, engineering, and product teams, to define requirements, validate model outputs, and deploy solutions in production.

Qualifications:

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.

• Technical Proficiency:

  • Strong background in NLP and machine learning, particularly with LLMs and foundation models (e.g., GPT, BERT, T5, etc.).

  • Proven experience in retrieval-augmented generation (RAG) systems, ranking algorithms, and data retrieval techniques.

  • Solid experience with frameworks and libraries such as TensorFlow, PyTorch, Hugging Face Transformers, LangChain, or similar tools.

• Modeling and Deployment Skills:

  • Expertise in fine-tuning and deploying large language models, building embeddings, and optimizing for speed and scalability.

  • Experience with MLOps and model deployment in cloud environments (e.g., AWS, GCP, Azure).

• Analytical Skills:

  • Strong understanding of ranking systems, information retrieval, and scoring mechanisms.

  • Proficient in model evaluation techniques and performance tuning, with an emphasis on relevance and accuracy.

• Problem-Solving Ability:

  • Ability to approach ambiguous problems with a structured and analytical mindset, drawing meaningful insights from unstructured data.

  • Demonstrated experience in building end-to-end ML solutions that integrate with larger data and engineering systems.

Why Join Us?

• Work on cutting-edge AI and machine learning challenges with a focus on LLMs and foundation models.

• Be part of an innovative team where your expertise and ideas directly influence product development and strategy.

• Competitive compensation and benefits, with opportunities for career growth in a rapidly evolving field.