Software Engineer, Compiler (AX Engineer)

About FuriosaAI FuriosaAI builds high-performance, high-efficiency AI compute for the Inference Era. Founded in 2017 by veteran semiconductor and AI algorithm engineers, Furiosa operates globally with offices in Korea and Silicon Valley, along with a compiler-focused R&D lab in Lisbon.

Our vision is to make AI computing sustainable, enabling access to powerful AI for everyone on Earth. We solve the AI hardware energy and operational cost crisis at the architectural level, rather than through brute force, building the world's first truly AI-native compute platform to unlock the full potential of artificial intelligence for every enterprise.

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]