About the Role

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

Software Engineer (Inference Engine) is responsible for developing and optimizing a high-performance inference engine for Large Language Models (LLMs) and multimodal LLMs running on FuriosaAI NPUs.

In this role, you will proactively research and apply the state-of-the-art inference optimization techniques to our inference engine. You will work in close collaboration with the compiler and hardware teams to enhance the engine's performance to its full potential.

Key Responsibilities

Design and implement FuriosaAI’s next-generation inference engine for large and multimodal language models—comparable in capability to frameworks such as vLLM and SGLang—optimized for throughput, latency, and memory efficiency.
Design and implement advanced inference optimizations—such as speculative decoding, KV-cache management, tensor/model parallelism, memory-efficient execution, and scheduling—in our production inference engine.
Design and develop capabilities for distributed and scalable inference, including prefill–decode (PD) and encode–prefill–decode (EPD) disaggregation, disaggregated speculative decoding, and hierarchical and external KV-cache storage such as HiCache and Mooncake.
Collaborate closely with the Compiler team to co-design and optimize execution for FuriosaAI NPUs, improving system-level throughput, latency, and memory utilization.
Proactively research, evaluate, and integrate state-of-the-art inference optimization techniques and key features of LLM serving frameworks into our production inference engine.

Minimum Qualifications

BS degree in Computer Science, Engineering, or a related field, with at least 3 years of relevant industry experience, or equivalent practical experience.
Proficiency in Rust or C++ programming skill.
Knowledge and passion of deep learning, LLM, and/or generative AI models.
Excellent problem-solving and data analysis skills.
Strong communication and collaboration skills.

Preferred Qualifications

Experience in building inference serving systems for large models, encompassing batching, scheduling, caching, and load balancing.
A deep understanding of performance optimization systems.
Proficiency in C++/CUDA or Triton kernel development.
Contributions to open-source inference frameworks such as vLLM, SGLang, or TensorRT-LLM.

Contact

recruit@furiosa.ai

Requirements

Rust or C++ proficiency

Proficiency in Rust or C++ programming skill is essential.

Deep learning knowledge

Knowledge and passion for deep learning, LLM, and/or generative AI models are required.

Problem-solving skills

Excellent problem-solving and data analysis skills are necessary.

Communication skills

Strong communication and collaboration skills are important for this role.

Nice to Have

Inference serving systems experience

Experience in building inference serving systems for large models is preferred.

Performance optimization understanding

A deep understanding of performance optimization systems is a plus.

C++/CUDA proficiency

Proficiency in C++/CUDA or Triton kernel development is advantageous.

Open-source contributions

Contributions to open-source inference frameworks such as vLLM or SGLang are beneficial.