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EvenUp

Builds AI software for personal injury lawyers

Senior Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • At least 5 years of experience building and deploying machine learning systems in production.
  • Strong software engineering fundamentals, including Python, distributed systems, and API design.
  • Experience owning the full machine learning 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, three days of your choice, in the San Francisco or Toronto office.

Responsibilities

  • Design, build, and own production machine learning systems across the full lifecycle, including 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 machine learning system designs.
  • Apply and productionize state-of-the-art techniques across natural language processing, information retrieval, and generative artificial intelligence where appropriate.
  • Define and implement evaluation strategies, including quality metrics, human-in-the-loop review, and automated benchmarks, to ensure model reliability.
  • Drive scalability and efficiency across machine learning workflows, from large-scale data processing to real-time inference.
  • Work with machine learning platform engineers to integrate models and frameworks into production environments.
  • Document system architectures and establish best practices for other engineers to build on.
  • Mentor other engineers and contribute technical judgment to hiring and calibration as the team grows.

Desired Qualifications

  • Experience with natural language processing, large language models, or generative artificial intelligence, including embeddings, fine-tuning (LoRA or other parameter-efficient fine-tuning), or prompt engineering.
  • Familiarity with vector databases such as Pinecone, Weaviate, FAISS, Milvus, Elasticsearch, or OpenSearch, or orchestration frameworks such as LangChain or LlamaIndex.
  • Experience with generative AI evaluation methodologies, including retrieval-augmented generation benchmarks, hallucination reduction, and factual grounding.
  • Experience in a high-growth startup environment.
  • Background in legal technology, healthcare, or other high-stakes, regulated domains.

About the Company

EvenUp

Builds AI software for personal injury lawyers

EvenUp builds AI software for personal injury law firms, the lawyers who represent people hurt in car crashes and other accidents, with tools for each step from intake to trial. In 2020, it was among the first companies to use AI to draft demand letters, the packages that lay out a victim's injuries, medical bills, and losses to open settlement talks with insurers. Its software, trained on past injury cases and medical records, reads long medical files to build treatment timelines, spot missed damages, and draft demands and court filings. Lawyers can also ask questions about a case through its Companion assistant, and firms pay a set price per case for the platform. EvenUp was founded in 2019 and is based in San Francisco.

Company size
501-1,000
Company stage
Series E
Total funding
$370M
Headquarters
San Francisco, CA
Founded
2019

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