Sr. AI Inference Systems Engineer

This role focuses on the end-to-end optimization of the inference pipeline for Large Models, including LLM and Multimodal. Key areas of focus include KV Cache storage strategies, Router architecture design, and collaborative operator optimization to maximize throughput and minimize latency. The role also involves conducting in-depth research into the inference logic of various hardware accelerators and evaluating their suitability for different inference scenarios to develop standardized optimization schemes. Additionally, the position requires designing and implementing high-performance inference frameworks, optimizing scheduling and memory management, and tracking global advancements in inference technology to drive productization of emerging technologies. Technical leadership is crucial, involving overcoming key technical bottlenecks, designing roadmaps, and mentoring team members to build a robust AI inference technical ecosystem.

• Lead the optimization of the full inference pipeline for Large Models (LLM, Multimodal); focus on KV Cache storage strategies, Router architecture design, and collaborative operator optimization to maximize throughput and minimize latency.
• Conduct in-depth research into the underlying inference logic of various hardware accelerators; evaluate architectural suitability for real-time, batch, and streaming inference scenarios to develop standardized optimization schemes.
• Design and implement high-performance inference frameworks; optimize scheduling and memory management to resolve long-tail issues such as communication latency and load imbalance in distributed inference.
• Track global advancements in inference technology (e.g., compiler optimization, model compression, and hardware fusion); drive the productization of emerging technologies within production environments.
• Lead efforts to overcome key technical bottlenecks in inference optimization; design technical roadmaps and mentor team members to build a robust AI inference technical ecosystem.

• Master’s or Ph.D. in Computer Science, Electronic Engineering, AI, or related fields; significant professional experience in AI inference optimization or heterogeneous computing.
• Proficient in at least one AI accelerator architecture; deep understanding of underlying principles, instruction sets, and hardware-specific tuning.
• Mastery of core inference optimization techniques, including multi-level KV Cache management, Quantization, and Intelligent Routing.
• Expert in parallel computing and distributed systems; deep understanding of low-level programming models (e.g., CUDA, Triton) and inference engine architectures.
• Familiar with mainstream deep learning frameworks (e.g., PyTorch, TensorFlow); experience in optimizing ultra-large-scale models is highly preferred.
• Stay current with global evolutions in inference technology and computing architectures, with the ability to objectively evaluate different technical paths.
• Strong analytical and cross-team collaboration skills, with a proven track record of leading complex inference projects to fruition.
• Experience in tuning ultra-large-scale inference clusters or driving AI inference productization; high-level publications or core patents in relevant fields are a plus.

• Sign on payment, relocation package, and restricted stock units, which will be evaluated on a case-by-case basis.
• Medical, dental, vision, life and disability benefits.
• Participation in the Company’s 401(k) plan.
• 15 to 25 days of vacation per year (depending on the employee’s tenure).
• 13 days of holidays throughout the calendar year.
• 10 days of paid sick leave per year.