Software Engineer
FuriosaAI
Software Engineer
FuriosaAI
Power-efficient AI inference accelerator chips
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.
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.
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
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
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)
The defining bottleneck of the AI era is building the right hardware and software stack to run it at global scale. Furiosa is solving this challenge holistically from the ground up.
With our flagship chip, RNGD, in mass production today and our next-generation platform in development with Broadcom, we are proving that full-stack, tensor-native compute is the future of AI infrastructure. This is a pivotal moment to join our team, right as we accelerate our global expansion.
At Furiosa, you will:
Solve AI’s Most Urgent Challenge. Help build the high-performance, energy-efficient inference hardware and software required to fulfill the promise of advanced AI.
Pioneer Full-Stack Co-Design. Work with teams that are architecting solutions from silicon up through the compiler (featuring innovations like Tensor Contraction Language and Virtual ISA) and serving frameworks.
Ship Real-World Silicon, Software, and Solutions. Turn breakthrough technology into commercial deployment. RNGD is in mass production with TSMC and running live enterprise workloads for global leaders like LG AI Research and Samsung SDS.
Partner With the Industry's Best. Collaborate across an elite global ecosystem that includes TSMC, Broadcom, SK Hynix, and GUC.
Do Your Life’s Best Work. Join a brilliant, low-ego, mission-driven team in a high-trust environment that values autonomy, intellectual curiosity, and shared ambition.
FuriosaAI develops AI accelerators for data centers. Its flagship RNGD chip is a power-efficient accelerator designed to boost performance per watt for running large language models and other demanding AI workloads. The hardware is configurable with multiple processing elements to fit different workloads, and it is paired with advanced compiler technology and profiling tools to identify bottlenecks and optimize efficiency. This combination of flexible hardware and software optimization differentiates FuriosaAI from competitors, focusing on maximizing performance while keeping energy use low. The company targets data centers and enterprises that need high-performance, energy-efficient AI solutions, and aims to make AI computing more sustainable and scalable by selling its chips and related software tools and pursuing long-term contracts with large-scale customers.
Explore more opportunities at FuriosaAI