The ad says“Experience with document understanding, entity/relationship extraction, or structured extraction from unstructured text”
The ad says“Experience with document understanding, entity/relationship extraction, or structured extraction from unstructured text”
The ad says“Experience with LLM fine-tuning techniques (LoRA, QLoRA, RLHF/RLVR) or advanced prompt engineering”
The ad says“Deep expertise in ML and NLP, including LLMs, with a track record of solving hard modeling problems - not just applying existing recipes”
The ad says“Experience with LLM fine-tuning techniques (LoRA, QLoRA, RLHF/RLVR) or advanced prompt engineering”
The ad says“High proficiency in Python and strong command of modern ML/NLP frameworks”
The ad says“Experience with LLM fine-tuning techniques (LoRA, QLoRA, RLHF/RLVR) or advanced prompt engineering”
The ad says“High proficiency in Python and strong command of modern ML/NLP frameworks”
The ad says“Experience with LLM fine-tuning techniques (LoRA, QLoRA, RLHF/RLVR) or advanced prompt engineering”
The ad says“Experience with LLM fine-tuning techniques (LoRA, QLoRA, RLHF/RLVR) or advanced prompt engineering”
The ad says“Experience with document understanding, entity/relationship extraction, or structured extraction from unstructured text”
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
Benefits
Flexible working hours to match your style
Offsites - get to meet your coworkers on a fully-expenses trip ever 6-12 months
A variety of virtual team events such as game nights & happy hours
Choice of medical, dental, and vision insurance plans for you and your family
Flexible paid time off and 10+ holidays per year
A stipend to upgrade your home office for fully-remote roles
401k for US-based employees
Preferred experience
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
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
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
Open to remote candidates or 3 days a week hybrid from our Toronto or San Francisco hubs
PhD in Machine Learning, Computer Science, or a related quantitative field
Experience with document understanding, entity/relationship extraction, or structured extraction from unstructured text
Experience with LLM fine-tuning techniques (LoRA, QLoRA, RLHF/RLVR) or advanced prompt engineering