Model research & prototyping – Explore, implement, and benchmark ML/NLP/generative-AI methods (e.g., LLM fine-tuning, retrieval-augmented generation, document understanding).
Data preparation & feature engineering – Clean, annotate, and transform structured and unstructured case data; build reusable datasets and data loaders.
Experimentation workflow – Design experiments, run A/B tests, analyze results, and communicate findings to the wider product and engineering teams.
Productionization – Help integrate models into our microservices architecture; collaborate with MLOps engineers on packaging, testing, monitoring, and scaling.
Cross-functional collaboration – Pair with product managers, legal analysts, and software engineers to translate pain points into ML solutions and measurable product improvements.
Continuous learning – Stay current with research in LLMs, representation learning, and prompt engineering; share insights through internal talks and docs.

Requirements

Education: Ph.D., M.S. or B.S. in Computer Science, Machine Learning, Data Science, Statistics, Computational Linguistics, or a closely related field
Core expertise:
Solid grounding in machine-learning fundamentals (supervised & unsupervised learning, evaluation metrics, overfitting/regularization).
Hands-on experience with NLP or generative-AI techniques (e.g., transformers, embeddings, sequence-to-sequence models, LLMs).
Technical stack:
Proficiency in Python and ML/NLP libraries such as PyTorch, TensorFlow, Hugging Face, spaCy, or similar.
Familiarity with SQL and basic data-engineering concepts (ETL, versioned datasets, notebooks).
Nice-to-have: exposure to cloud platforms (AWS/GCP), experiment-tracking tools (Weights & Biases, MLflow), or containerized deployment (Docker/Kubernetes).
Mindset & people skills:
Eagerness to learn from senior teammates and iterate quickly in a fast-moving startup.
Clear, concise communication—both written and verbal.
Strong analytical thinking and a bias toward shipping pragmatic, high-impact solutions.

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningnatural language processinggenerative AILLM fine-tuningdata preparationfeature engineeringA/B testingsupervised learningunsupervised learningevaluation metrics

Soft Skills

clear communicationanalytical thinkingcollaborationeagerness to learniterative mindset