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# Software Engineer, AI/ML GenAI

- **Company:** Recruiting from Scratch
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $175,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Design of User Interfaces, Human-Computer Interaction, Python (Programming Language), Machine Learning, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Backend
- **Published:** November 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/software-engineer-ai-ml-genai-san-francisco-ca--05d40e60-63e8-4d59-9e9d-c3a06eadc90e

## About the Role

Software Engineering, Artificial Intelligence (AI), Software Engineering, Artificial Intelligence (AI), Funding, Grant Writing, Nonprofit, Grant Administration/Management, User Interface/Experience (UI/UX), Process Improvement, * 5-8 years of experience in software engineering with a strong emphasis on Python.

 * Proven track record of building and deploying LLM-powered applications in production environments.
 * Hands-on experience with vector databases and embeddings, demonstrating expertise in AI system reliability and performance.
 * Ability to design complex backend systems with a focus on scaling and monitoring AI features.
 * Experience working in fast-paced startup environments with a strong initiative and ownership mentality.

Preferred

 * Familiarity with machine learning evaluation methods and observability practices for AI systems.
 * Experience collaborating closely with product and design teams to deliver impactful user-facing features.
 * Understanding of operational discipline in maintaining AI systems post-launch.

## Description

* Design, develop, and scale production-ready AI features that enhance user experience and operational efficiency.
 * Implement retrieval-augmented generation pipelines, ensuring robust ingestion, chunking, retrieval, and tuning processes.
 * Collaborate with cross-functional teams to build user-facing features that address real customer challenges.
 * Own the end-to-end lifecycle of features from ideation through to deployment, monitoring, and iteration.
 * Evaluate model outputs and establish feedback loops to continuously improve AI quality and reliability.
 * Utilize Python and vector databases to create efficient and scalable backend systems for AI applications.