Machine Learning Engineer
- Toronto, ON, Canada
$130,000 to $205,000 USD / year · Full-time
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Job details
- Pay
- $130,000 to $205,000 USD / year
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
Original job description
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.
Machine Learning Engineer
We're looking for a Machine Learning Engineer to join our ML team at EvenUp. You'll develop and deploy models that power Piai™, our proprietary claims-intelligence platform, with a focus on machine learning, natural-language processing, and generative AI.
Working alongside senior ML engineers, data scientists, and legal subject-matter experts, you'll turn raw legal and medical data into production-ready models that directly improve outcomes for personal-injury clients.
What You'll Do
As part of our total rewards package, we offer attractive benefits and perks to our employees, including:
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: $130K - $205K
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.
Machine Learning Engineer
We're looking for a Machine Learning Engineer to join our ML team at EvenUp. You'll develop and deploy models that power Piai™, our proprietary claims-intelligence platform, with a focus on machine learning, natural-language processing, and generative AI.
Working alongside senior ML engineers, data scientists, and legal subject-matter experts, you'll turn raw legal and medical data into production-ready models that directly improve outcomes for personal-injury clients.
What You'll Do
- 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 our 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.
- 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.
- 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.
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.
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: $130K - $205K
More about this job
Responsibilities
Develop, benchmark, and help deploy machine learning, NLP, and generative AI models for a legal claims-intelligence platform, including LLM fine-tuning, retrieval-augmented generation, and document understanding. Prepare reusable datasets, design and analyze experiments, support production model integration, and collaborate with product, legal, data science, and engineering teams to improve measurable outcomes.
Requirements
A degree in computer science, machine learning, data science, statistics, computational linguistics, or a related field is required, along with a solid foundation in machine learning and hands-on NLP or generative AI experience. Candidates should be proficient in Python and relevant ML/NLP libraries, familiar with SQL and basic data engineering, and able to work hybrid from the San Francisco or Toronto office.
Skills
- Machine Learning
- Natural Language Processing
- Generative AI
- Large Language Models
- Python
- PyTorch
- TensorFlow
- Hugging Face
- spaCy
- SQL
- Data Engineering
- Experiment Design
- A/B Testing
- Model Evaluation
- Retrieval-Augmented Generation
- Document Understanding
Benefits
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Life Insurance
- Accident Insurance
- Critical Illness Insurance
- Flexible Paid Time Off
- Sick Leave
- Short-Term Disability
- Long-Term Disability
- Paid Holidays
- Home Office Stipend
- 401(k)
- RRSP
- Paid Parental Leave
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
- Legal
Keywords
- Machine Learning
- Natural Language Processing
- Generative AI
- Large Language Models
- LLM Fine-Tuning
- Retrieval-Augmented Generation
- Document Understanding
- Transformers
- Embeddings
- Sequence-to-Sequence Models
- Python
- PyTorch
- TensorFlow
- Hugging Face
- spaCy
- SQL
- ETL
- Versioned Datasets
- Data Loaders
- Supervised Learning
- Unsupervised Learning
- Evaluation Metrics
- Regularization
- A/B Testing
- AWS
- GCP
- Weights & Biases
- MLflow
- Docker