Staff Machine Learning Engineer, EvenUp

• 7+ years of hands-on ML engineering experience, with multiple models shipped and running in production
• Deep expertise in ML and NLP, including LLMs, with a track record of solving hard modeling problems - not just applying existing recipes
• High proficiency in Python and strong command of modern ML/NLP frameworks
• Demonstrated ability to set technical strategy and drive execution in ambiguous, fast-moving environments
• A track record of mentoring engineers and raising technical standards beyond your own output
• Experience partnering directly with Product and Engineering leadership, not just executing their asks
• Open to remote candidates or 3 days a week hybrid from our Toronto or San Francisco hubs

Desirable

• Join EvenUp as a Staff Machine Learning Engineer and help set the technical direction for how machine learning powers Piai™, our proprietary claims-intelligence platform
• This is a technical leadership role - you'll shape modeling strategy across a broad problem space, turning raw legal and medical data into production systems that improve outcomes for personal-injury clients
• You'll partner closely with Product, Research, and Engineering leaders to set strategy, and you'll be a technical anchor for the broader ML team - setting standards, mentoring senior engineers, and driving decisions that shape both product outcomes and company growth
• Set technical strategy for a broad area of the ML roadmap, translating ambiguous business and research goals into scoped, production-ready systems
• Tackle the hardest modeling problems in the org - complex reasoning, long-context and multi-document understanding, or other frontier challenges as they come up
• Apply advanced ML techniques - fine-tuning, reinforcement learning, retrieval, or others - and know when a technique is the right tool versus over-engineering
• Establish rigorous evaluation standards, reducing hallucinations, improving factual consistency, and defining what "good" looks like for a given system
• Drive data excellence through hands-on analysis of training and evaluation data, managing noise, edge cases, and drift at scale
• Provide technical leadership and mentorship across the ML team, raising the bar for experimentation, benchmarking, and engineering rigor
• Act as the bridge between research and production - ensuring new techniques get integrated into shippable systems, not just proofs of concept
• Partner cross-functionally with product, engineering, and legal subject-matter experts to set technical direction
• Cost effectively scale practical machine learning systems in a hyper-growth environment, ensuring they remain grounded in real business and customer needs