Senior Machine Learning Engineer

EvenUp•San Francisco, CA

34d•$196,000 - $265,000•Hybrid

About The Position

At EvenUp, we leverage cutting-edge AI to bring fairness and accessibility to the legal system. Tackling the most complex legal document challenges requires expertise in data quality, robust model development, and ongoing innovation. We're looking for a Senior Machine Learning Engineer to help build the models and systems powering Piai™, our proprietary claims-intelligence platform. You'll work across the ML stack - from data pipelines to production model deployment - alongside ML engineers, data scientists, and legal subject-matter experts to turn raw legal and medical data into systems that directly improve outcomes for personal-injury clients.

Requirements

5+ years building and deploying machine learning systems in production.
Strong software engineering fundamentals - Python, distributed systems, API design.
Experience owning the full ML lifecycle, not just model training in isolation.
A track record of turning ambiguous problems into scoped, shippable solutions.
Experience mentoring other engineers and influencing technical direction beyond your own code.
Ability to work hybrid (3 days your choice) in our San Francisco or Toronto Canada office.

Nice To Haves

Experience with NLP, LLMs, or generative AI - embeddings, fine-tuning (LoRA or other PEFT), or prompt engineering.
Familiarity with vector databases (Pinecone, Weaviate, FAISS, Milvus, Elasticsearch/OpenSearch) or orchestration frameworks (LangChain, LlamaIndex).
Experience with evaluation methodologies for generative AI - RAG benchmarks, hallucination reduction, factual grounding.
Experience in a high-growth startup environment.
Background in legal tech, healthcare, or other high-stakes, regulated domains.

Responsibilities

Design, build, and own production ML systems across the full lifecycle - problem framing, data strategy, training, evaluation, deployment, and monitoring.
Architect scalable data pipelines that handle structured, unstructured, and embeddings-based data for training and inference.
Build reusable frameworks and infrastructure for model development, evaluation, and benchmarking.
Partner with data scientists and product managers to translate ambiguous business problems into concrete ML system designs.
Apply and productionize state-of-the-art techniques across NLP, information retrieval, and generative AI where the problem calls for it.
Define and implement evaluation strategies - quality metrics, human-in-the-loop review, automated benchmarks - to ensure model reliability.
Drive scalability and efficiency across ML workflows, from large-scale data processing to real-time inference.
Work with ML platform engineers to integrate models and frameworks into production environments.
Document system architectures and establish best practices that other engineers build on.
Mentor other engineers and contribute technical judgment to hiring and calibration as the team grows.