Senior Machine Learning Engineer , EvenUp

• 5+ years building and deploying machine learning systems in production
• Strong software engineering fundamentals - Python, distributed systems, API design
• Experience owning the full ML lifecycle, not just model training in isolation
• A track record of turning ambiguous problems into scoped, shippable solutions
• Experience mentoring other engineers and influencing technical direction beyond your own code
• Ability to work hybrid (3 days your choice) in our San Francisco or Toronto Canada office

Desirable

• At EvenUp, we leverage cutting-edge AI to bring fairness and accessibility to the legal system
• Tackling the most complex legal document challenges requires expertise in data quality, robust model development, and ongoing innovation
• We're looking for a Senior Machine Learning Engineer to help build the models and systems powering Piai™, our proprietary claims-intelligence platform
• You'll work across the ML stack - from data pipelines to production model deployment - alongside ML engineers, data scientists, and legal subject-matter experts to turn raw legal and medical data into systems that directly improve outcomes for personal-injury clients
• Design, build, and own production ML systems across the full lifecycle - problem framing, data strategy, training, evaluation, deployment, and monitoring
• Architect scalable data pipelines that handle structured, unstructured, and embeddings-based data for training and inference
• Build reusable frameworks and infrastructure for model development, evaluation, and benchmarking
• Partner with data scientists and product managers to translate ambiguous business problems into concrete ML system designs
• Apply and productionize state-of-the-art techniques across NLP, information retrieval, and generative AI where the problem calls for it
• Define and implement evaluation strategies - quality metrics, human-in-the-loop review, automated benchmarks - to ensure model reliability
• Drive scalability and efficiency across ML workflows, from large-scale data processing to real-time inference
• Work with ML platform engineers to integrate models and frameworks into production environments
• Document system architectures and establish best practices that other engineers build on
• Mentor other engineers and contribute technical judgment to hiring and calibration as the team grows