Jobs · Information Technology · California

Senior AI Software Engineer

Harnham · San Francisco County, CA · 3 days ago
HybridInformation Technology$240k/yrFull-time

About the role

We're partnering with a high-growth, mission-driven SaaS company that's transforming business trust through AI. This role involves defining AI architecture and impacting production systems.

Responsibilities

  • Design and own production AI systems, including LLM pipelines, retrieval systems, and orchestration layers
  • Build and scale RAG systems, reranking pipelines, and vector-based search infrastructure
  • Define evaluation frameworks to measure retrieval quality, reasoning accuracy, and system performance
  • Analyze production behavior, identify failure modes, and drive improvements based on data
  • Make key architectural decisions across model infrastructure, tooling, and workflows
  • Partner closely with product, platform, and domain teams to translate complex requirements into scalable systems
  • Lead best practices for building reliable, observable, and cost-efficient AI systems

Requirements

  • 6+ years of software engineering experience, including 3+ years working on ML or AI systems
  • Proven experience owning and deploying production LLM systems
  • Strong background in RAG, embeddings, reranking, and vector databases (e.g., Pinecone, FAISS, Chroma)
  • Experience designing evaluation systems and improving models through quantitative analysis
  • Strong Python skills, with solid software engineering fundamentals
  • Experience making architectural decisions that influence team or org direction
  • Strong understanding of production systems, including reliability, observability, and cost tradeoffs
  • Able to break down ambiguous problems and operate with a high degree of ownership
  • Clear communication skills and experience working cross-functionally

Nice to Have

  • Experience in regulated domains such as compliance or security
  • Familiarity with data platforms or analytics tooling
  • Experience with orchestration frameworks (e.g., Temporal, Airflow)
  • Exposure to LLM evaluation platforms or tooling
  • Contributions to open source, research, or technical communities

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