About this role
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)
CONTACT
- [email protected]
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)
CONTACT
- [email protected]
Tech stack
LLM
About FuriosaAI
FuriosaAI is hiring for the software engineer, compiler role. NewJob aggregates active openings directly from FuriosaAI's applicant tracking system, so this listing is current.
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