About The Role
The Compiler team at FuriosaAI builds the software stack that enables ML models to run at peak performance on our AI accelerator hardware. This is a newly established role — there's no playbook yet. You'll be the first person to define and operationalize AI-assisted engineering workflows for the team: identifying high-leverage bottlenecks, running experiments, and shipping repeatable tooling + playbooks that stick. While you’ll be embedded in the Compiler team, compiler specialization is not required — your scope can span development, code review, debugging, CI/testing, documentation, and project execution.
If you're the kind of engineer who gets uncomfortable without a clear job description — this role isn't for you. If you're the kind who sees an undefined space and immediately starts mapping it — read on.
Responsibilities
- Identify pain points and leverage opportunities across the engineering workflow (development, code review, debugging, CI/testing, documentation, and project execution)
- Research, prototype, and benchmark AI/automation tools (e.g., coding agents, LLM-assisted review, debugging assistants) against real team workflows
- Design and maintain team-specific prompt libraries, workflow templates, and integration guides (IDE, code review, CI, debugging, documentation)
- Improve CI/testing signal quality and feedback loops (e.g., flaky test reduction, failure triage workflows)
- Lead onboarding sessions, regular Q&A sessions, and internal knowledge sharing for newly adopted tools
- Define and track adoption + impact metrics (e.g., PR cycle time, review turnaround, time-to-triage regressions, CI flakiness), and iterate based on data and feedback
- Monitor the AI tooling ecosystem and surface relevant developments to the team
Minimum Qualifications
- 3+ years of software engineering experience
- Hands-on experience with LLM-based coding tools (e.g., GitHub Copilot, Cursor, Claude Code, Codex, or equivalent)
- Strong understanding of software development workflows from the perspective of a practicing engineer
- Ability to rapidly evaluate new tools and translate findings into actionable team guidance
Preferred Qualifications
- Background in compiler engineering, systems programming, or static analysis
- Experience building or customizing LLM-based agents or tooling via API
- Track record of driving internal tooling adoption or developer experience improvements
- Experience producing technical documentation, runbooks, or internal tech talks
- Experience improving engineering workflows (DevOps, CI/CD, developer productivity, or technical program execution)
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