Senior Machine Learning Engineer - Systems

EvenUp EvenUp · Vertical AI · San Francisco, CA · Hybrid · Data Science

Senior Machine Learning Engineer to develop and deploy end-to-end ML systems for retrieval-augmented generation (RAG), vector search, and fine-tuning pipelines, integrating structured, unstructured, and embeddings-based data. The role involves building frameworks for data extraction, evaluation, and benchmarking of LLMs, translating business problems into ML system designs, and researching state-of-the-art techniques in semantic search and prompt engineering. Responsibilities include implementing evaluation strategies, ensuring scalability of embedding generation and retrieval pipelines, and integrating ML frameworks into production.

What you'd actually do

  1. Design and implement end-to-end ML systems for retrieval-augmented generation (RAG), vector search, and fine-tuning pipelines.
  2. Build and optimize data pipelines that integrate structured, unstructured, and embeddings-based data into ML workflows.
  3. Develop frameworks and reusable components for data extraction, evaluation, and benchmarking of LLMs and other models.
  4. Collaborate with data scientists and product teams to translate business problems into ML system designs.
  5. Research, prototype, and productionize state-of-the-art techniques in semantic search, embeddings, and prompt engineering.

Skills

Required

  • Python
  • distributed computing
  • APIs
  • transformer models
  • LLMs
  • embeddings
  • fine-tuning methods like LoRA, PEFT
  • evaluation methodologies for generative AI
  • RAG benchmarks
  • hallucination reduction
  • factual grounding

Nice to have

  • vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus, Elasticsearch/OpenSearch)
  • retrieval frameworks (LangChain, LlamaIndex, custom retrieval pipelines)

What the JD emphasized

  • end-to-end ML systems
  • data pipelines
  • frameworks and reusable components
  • evaluation strategies
  • scalability and efficiency
  • state-of-the-art techniques

Other signals

  • develop and deploy models that power Piai
  • focus on machine learning, natural-language processing, and generative AI
  • turn raw legal and medical data into production-ready models