Machine Learning Engineer 1 & 2 - Systems

EvenUp is on a mission to close the justice gap using technology and AI. They are looking for an early career Data Scientist / Machine Learning Engineer to join their AI R&D team, focusing on developing and deploying models that enhance their claims-intelligence platform.

Responsibilities

• 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

Skills

• Ph.D., M.S. or B.S. in Computer Science, Machine Learning, Data Science, Statistics, Computational Linguistics, or a closely related field
• 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)
• 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)
• 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
• Exposure to cloud platforms (AWS/GCP)
• Experiment-tracking tools (Weights & Biases, MLflow)
• Containerized deployment (Docker/Kubernetes)

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

Company Overview