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Senior Machine Learning Engineer

EvenUp · United States

External listingfull-time24 days ago

About The Role

Join EvenUp, a company dedicated to bringing fairness and accessibility to the legal system through cutting-edge AI technology. As a Senior Machine Learning Engineer, you will play a crucial role in building the models and systems that power our proprietary claims-intelligence platform, Piai™. You will work across the entire ML stack, from data pipelines to production model deployment, and collaborate with ML engineers, data scientists, and legal experts to improve outcomes for personal-injury clients.

  • Design, build, and own production ML 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.
  • Partner with data scientists and product managers to translate ambiguous business problems into concrete ML system designs, applying and productionizing state-of-the-art techniques across NLP, information retrieval, and generative AI.
  • Ability to work hybrid (3 days your choice) in our San Francisco or Toronto Canada office
  • Experience owning the full ML lifecycle, not just model training in isolation
  • Strong software engineering fundamentals - Python, distributed systems, API design
  • Experience mentoring other engineers and influencing technical direction beyond your own code
  • A track record of turning ambiguous problems into scoped, shippable solutions
  • 5+ years building and deploying machine learning systems in production
  • 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

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