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# Senior Software Engineer, Training & Experimentation

- **Company:** Lightning AI
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $180,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Applications Architecture, Automation of Tests, Microsoft Azure, Cloud Computing, Software Quality, Continuous Delivery, Programming Tools, Distributed Systems, Python (Programming Language), Technical Debt, Backend, Containerization, Automation Anywhere, Docker
- **Published:** August 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=81afdaa11fa767d1

## About the Role

* Extensive experience building scalable backend systems using Go, Python, Rust, or similar languages.
 * Strong understanding of distributed systems, asynchronous processing, APIs, and cloud-native application architecture.
 * Experience designing and operating production services at scale.
 * Hands-on experience building backend platforms, developer tools, or infrastructure products.
 * Familiarity with cloud platforms such as AWS, GCP, or Azure and container technologies including Docker and Kubernetes.
 * Proven ability to take ownership of large technical initiatives from architecture through production.
 * Commitment to software quality, automated testing, observability, and continuous delivery.
 * Strong communication skills and the ability to collaborate effectively across engineering, product, research, and design teams.
 * Ability to thrive in fast-moving, ambiguous environments while balancing technical excellence with product impact.

## Description

* Design and build backend services that power AI agent orchestration and execution.
 * Develop scalable APIs and platform capabilities for tool use, workflow orchestration, memory, and state management.
 * Build reliable infrastructure that enables long-running, distributed AI workflows.
 * Partner with product, research, and platform engineering teams to bring new agent capabilities into production.
 * Improve platform reliability, observability, and performance as customer workloads continue to scale.
 * Evaluate and strengthen technical architecture, engineering processes, and developer tooling.
 * Maintain high standards for software quality through testing, automation, continuous delivery, and proactive management of technical debt.
 * Mentor engineers on system design, distributed systems, and backend engineering best practices


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