Senior Applied ML Engineer

Who We Are

Join a team that puts its People First! Since 1889, First American (NYSE: FAF) has held an unwavering belief in its people. They are passionate about what they do, and we are equally passionate about fostering an environment where all feel welcome, supported, and empowered to be innovative and reach their full potential. Our inclusive, people-first culture has earned our company numerous accolades, including being named to the Fortune 100 Best Companies to Work For® list for ten consecutive years.

We have also earned awards as a best place to work for women, diversity and LGBTQ+ employees, and have been included on more than 50 regional best places to work lists. First American will always strive to be a great place to work, for all. For more information, please visit

Be part of a transformative team that is shaping the way First American builds and delivers world-class technology products that fuel the real estate industry. You'll be responsible for designing, building, and deploying applied machine learning solutions—including deep learning transformer-based models for Natural Language Processing and Computer Vision, as well as traditional shallow learning models. The role focuses on developing scalable ML systems that deliver measurable business outcomes and drive value across the organization.

• Design, build, fine-tune, and deploy state-of-the-art machine learning and large language models at scale, supporting millions of daily predictions with a strong focus on accuracy, latency, compute efficiency, and cost optimization.
• Develop end-to-end ML and LLM pipelines, covering data ingestion, scripting, automated workflows for OCR, model training, evaluation, and post-processing in production environments.
• Build and operationalize LLM fine-tuning pipelines, applying a range of model adaptation techniques including full fine-tuning, LoRA (Low-Rank Adaptation), prompt-based methods, and Direct Preference Optimization (DPO).
• Design and experiment with novel LLM architectures, balancing model size, computational efficiency, memory constraints, and deployment requirements.
• Optimize LLMs for production deployment through model quantization, compression, and teacher–student architectures, enabling efficient inference in resource-constrained environments.
• Architect and deploy Retrieval-Augmented Generation (RAG) systems, leveraging vector databases, embedding services, semantic search, document chunking, indexing, and retrieval mechanisms using frameworks such as Lang Chain, Llama Index, and commercial RAG platforms within GCP and Databricks.
• Innovate in ML operations and evaluation, including automated ground-truth generation, continuous post-evaluation pipelines, and iterative feedback loops to systematically improve model performance over time.
• Design and implement CI/CD pipelines for machine learning systems, ensuring high availability, reliability, low latency, and rapid iteration from experimentation to production.

• 5+ years of experience in machine learning engineering, with a proven track record of deploying and operating ML and NLP/LLM systems in production at scale.
• Strong hands‑on experience building full‑stack ML systems, from data ingestion and automation to training, evaluation, deployment and monitoring.
• Deep expertise in LLM fine‑tuning and adaptation techniques, including full fine‑tuning, LoRA, prompt‑based optimization, and preference‑based methods such as DPO.
• Practical experience designing and optimizing LLM architectures, with an emphasis on compute efficiency, memory usage, and real‑world deployment constraints.
• Demonstrated proficiency in model inference optimization, including quantization, compression, and distillation techniques for high‑throughput, cost‑efficient production systems.
• Solid understanding and hands‑on experience with RAG architectures, vector stores, embeddings, semantic search, chunking strategies, and retrieval workflows integrated with large language models.
• Experience using modern LLM orchestration and RAG frameworks such as Lang Chain, Llama Index, and managed AI platforms within cloud ecosystems like GCP and…