Machine Learning Engineer

About This Role

This Machine Learning Engineer role involves developing and deploying models for Piai, the company's proprietary claims-intelligence platform, with a focus on machine learning, natural-language processing, and generative AI. Working with senior ML engineers, data scientists, and legal subject-matter experts, you will transform raw legal and medical data into production-ready models that directly improve outcomes for personal-injury clients.

Responsibilities

• Explore, implement, and benchmark ML, NLP, and generative AI methods, including LLM fine-tuning, retrieval-augmented generation, and document understanding
• Clean, annotate, and transform structured and unstructured case data, and build reusable datasets and data loaders
• Design experiments, run A/B tests, analyze results, and communicate findings to product and engineering teams
• Help integrate models into production systems, partnering with ML platform engineers on testing, monitoring, and scaling
• Pair with product managers, legal analysts, and software engineers to translate real problems into ML solutions and measurable improvements
• Stay current with research in LLMs, representation learning, and prompt engineering, and share what you learn with the team

Requirements

• B.S., M.S., or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, Computational Linguistics, or a related field
• Solid grounding in ML fundamentals: supervised and unsupervised learning, evaluation metrics, overfitting and regularization
• Hands-on experience with NLP or generative AI techniques: transformers, embeddings, sequence-to-sequence models, or LLMs
• Proficiency in Python and ML/NLP libraries such as PyTorch, TensorFlow, Hugging Face, or spaCy
• Familiarity with SQL and basic data engineering concepts: ETL, versioned datasets, notebooks
• Strong analytical thinking, clear communication, and eagerness to learn and iterate quickly in a fast-moving startup
• Ability to work hybrid (3 days your choice) in our San Francisco or Toronto, Canada office

Nice to have:

• Exposure to cloud platforms (AWS, GCP)
• Experience with experiment-tracking tools (Weights & Biases, Gemini Enterprise Agent Platform, MLflow)
• Familiarity with containerized deployment (Docker, Kubernetes)
• Experience in a high-growth startup environment

Benefits

• Choice of medical, dental, and vision insurance plans for you and your family
• Additional insurance coverage options for life, accident, or critical illness
• Flexible paid time off, sick leave, short-term and long-term disability
• 10 US observed holidays, and Canadian statutory holidays by province