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Staff Machine Learning Engineer

Toronto Full-time AI Solutions Lead
Salary
$212,000-$301,000/yr
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Original posting on LinkedIn

EvenUp is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more.

We are one of the fastest-growing vertical SaaS companies in history, and we are just getting started. EvenUp is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, SignalFire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn more at www.evenuplaw.com.

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.

What You'll Do

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.

What You Bring

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.

Nice to Have

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.
Experience in a high-growth startup environment.
Open to remote candidates or 3 days a week hybrid from our Toronto or San Francisco hubs.

Benefits & Perks

As part of our total rewards package, we offer attractive benefits and perks to our employees, including:

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.
A home office stipend.
401(k) for US-based employees and RRSP for Canada-based employees.
Paid parental leave.
A local in-person meet-up program.
Hubs in San Francisco and Toronto.

(Please note the above benefits & perks are for full-time employees)

Notice to Candidates

To ensure fairness and proper consideration, we do not accept resumes or expressions of interest via email or social media messages. If you’re interested in a role, please submit your application directly through our careers page.

Please note that EvenUp may use AI notetakers and other recording devices in the recruiting process. If you interview with us, with your consent, we may record your conversations and summarize them into notes for internal use. Recording is optional, and declining will not affect your candidacy.

EvenUp is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Compensation Range: $212K - $301K

Listing structured with AI support from LinkedIn. Always confirm the conditions with the company before applying.
FAQ

Frequently asked questions

Who offers this AI Solutions Lead role in Toronto?

The role is posted by EvenUp from LinkedIn. aiManagerJobs is a directory that collects, structures and links to the original source, it is not the employer. Hiring is handled by the company.

How much does this AI Solutions Lead role pay?

The salary stated in the posting is $212,000-$301,000/yr. It is indicative and worth confirming with EvenUp before applying.

What type of role is it?

This is a ai solutions lead position on a full-time basis in Toronto. The exact schedule and conditions are in the original posting from the company.

How do I apply for this role in Toronto?

Use the apply button to go to the original source (LinkedIn) and follow the company instructions. You can also create an alert and receive new Toronto roles by email.

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