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# Software Engineer, Developer Productivity

- **Company:** Consensus Inc
- **Location:** Belmont, CA, United States
- **Experience:** Expert
- **Salary:** $190,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Cloud Computing, Code Review, Continuous Integration, Cursor (Graphical User Interface Elements), Programming Tools, Github, Python (Programming Language), Datadog, Google Cloud, Large Language Models, Backend, Fastapi, Kubernetes, Terraform, Docker
- **Published:** September 4, 2026
- **Apply:** https://www.disabledperson.com/jobs/74812750-software-engineer-developer-productivity

## About the Role

* 5+ years of engineering experience, including meaningful time on infrastructure or developer tooling
 * You work AI-natively: coding agents and LLM tooling are part of your daily workflow, and you have sharp instincts for where AI removes toil vs. where it can't yet be trusted
 * Deep experience with Kubernetes, Terraform, Docker, and modern CI/CD on a major cloud provider
 * Empathy for what slows engineers down, and a track record of fixing it
 * History of high-velocity delivery and ownership with minimal direction
 * Interest in the mission: science, research, education
 * Bonus: Python/FastAPI backend, ML or search infrastructure, or having rolled out AI dev tooling (agent workflows, evals, MCP) to a team

## Description

Engineer whose job is to make the rest of the engineering team faster. You'll own our cloud infrastructure, CI/CD, and observability (foundation on GCP, Kubernetes, Terraform, and Datadog), but most of your time will go toward making Consensus an AI-native engineering org: rolling out coding agents, keeping our codebase agent-ready, and building automations that absorb routine work so engineers and agents can both ship with confidence., * Accelerate the team with AI. Evaluate and roll out AI tooling across engineering (coding agents like Claude Code / Cursor / Codex / Devin, AI-powered code review and testing), tune it to our codebase and conventions, and keep the repo agent-ready with high-quality docs, skills, and guardrails.

 * Build internal agents and automations that absorb toil: incident triage, test maintenance, on-call support, codebase Q&A. Track adoption and measure the productivity gains.
 * Own the developer loop: GitHub-based CI/CD, build speed, release process, dev environments, and load/automated testing (including AI-driven end-to-end tests). Make it fast to go from idea to production.
 * Keep the platform reliable. Own cloud infra (GCP, Kubernetes, Terraform), observability and incident response (Datadog), and extend coverage to newer surfaces like LLM pipelines and agent workloads.


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