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# Senior Machine Learning Engineer

- **Company:** Evenup Inc
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Big Data, Document Management Systems, Distributed Systems, Elasticsearch, Information Retrieval, Python (Programming Language), Machine Learning, Performance Tuning, Azure Machine Learning, Pinecone, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Prompt Engineering, Hallucination Detection, Llamaindex, Generative AI, Data Strategy, Kubernetes, Real-time Inference, Weaviate, Milvus, Machine Learning Operations, FAISS, Api Design, Data Pipelines, OpenSearch
- **Published:** October 15, 2026
- **Apply:** https://www.dice.com/job-detail/95679b10-a169-47af-9029-fce68b265868

## About the Role

* 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., * 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.

## Description

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.

What You'll Do

 * 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.