Job description

ABOUT THE JOB We are seeking a highly technical Inference Engine Engineer to optimize the performance and efficiency of our core inference engine. In this role, you will focus on designing, implementing, and optimizing GPU kernels and supporting infrastructure for next-generation generative and agentic AI workloads. Your work will directly power the most latency-critical and compute-intensive systems deployed by our customers. We are looking for an exceptional engineer with a strong foundation in GPU programming and compiler infrastructure. The ideal candidate enjoys pushing performance boundaries and has experience supporting production-scale machine learning applications. KEY RESPONSIBILITIES - Design and optimize custom GPU kernels for AI (e.g., transformer and diffusion) workloads - Contribute to the development of FriendliAI’s kernel compiler, memory planner, runtime, and other core components. - Collaborate with cloud and infrastructure engineers to ensure end-to-end inference performance - Analyze performance bottlenecks across the software and hardware stack, and implement targeted optimizations - Drive support for new model architectures and tensor compute patterns - Maintain production-grade performance infrastructure, including profiling, benchmarking, and validation tools QUALIFICATIONS - 5+ years of experience in production or high-impact research environments - Production-level expertise in Python and C++ - Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent - Experience developing machine learning frameworks or performance-critical runtime systems - Hands-on experience writing and optimizing GPU kernels - Hands-on experience profiling GPU kernels - Experience working with generative AI models such as transformer and diffusion models PREFERRED EXPERIENCE - Experience developing machine learning compilers or code generation systems - Familiarity with dynamic shape compilation, memory planning, and kernel fusion - Contributions to inference engines, compilers, or high-performance numerical libraries - Understanding of multi-GPU and distributed inference strategies BENEFITS - Flexible working hours - Daily lunch and dinner provided; unlimited snacks and beverages - Supportive and highly collaborative work environment - Health check-up support and top-tier equipment/hardware support - A front-row seat to the generative AI infrastructure revolution - Competitive compensation, startup equity, health insurance, and other benefits. ABOUT FRIENDLIAI FriendliAI is building the world’s best AI inference platform that makes large language and multi-modal models fast, efficient, and deployable at scale. We power high-throughput, low-latency AI workloads for organizations worldwide and integrate directly with Hugging Face, giving developers instant access to over 600,000 open-source models. We are a small, fast-moving team doing work that matters at one of the most exciting moments in the history of technology. With our world-class inference engine, we are building a platform that the AI industry can actually rely on.

Requirements

Must-haves

Design and optimize custom GPU kernels for AI (e.g., transformer and diffusion) workloads
Contribute to the development of FriendliAI’s kernel compiler, memory planner, runtime, and other core components.
Collaborate with cloud and infrastructure engineers to ensure end-to-end inference performance
Analyze performance bottlenecks across the software and hardware stack, and implement targeted optimizations
Drive support for new model architectures and tensor compute patterns
Maintain production-grade performance infrastructure, including profiling, benchmarking, and validation tools
5+ years of experience in production or high-impact research environments
Production-level expertise in Python and C++
Bachelor’s or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
Experience developing machine learning frameworks or performance-critical runtime systems
Hands-on experience writing and optimizing GPU kernels
Hands-on experience profiling GPU kernels
Experience working with generative AI models such as transformer and diffusion models

Nice-to-haves

Experience developing machine learning compilers or code generation systems
Familiarity with dynamic shape compilation, memory planning, and kernel fusion
Contributions to inference engines, compilers, or high-performance numerical libraries
Understanding of multi-GPU and distributed inference strategies

Application prep

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Keywords to mirror

aimachine learningpython

Platform: ashby
Effort: quick

Benefits

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Health Insurance