Business UnitWhat the Role Entails- End-to-End Inference Optimization: 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.
- Heterogeneous Computing Research: 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.
- Inference Framework & Toolchain: 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.
- Technological Innovation: Track global advancements in inference technology (e.g., compiler optimization, model compression, and hardware fusion); drive the productization of emerging technologies within production environments.
- Technical Leadership: 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.
Who We Look For- Education & Experience: Master's or Ph.D. in Computer Science, Electronic Engineering, AI, or related fields; significant professional experience in AI inference optimization or heterogeneous computing.
- Hardware Expertise: Proficient in at least one AI accelerator architecture; deep understanding of underlying principles, instruction sets, and hardware-specific tuning.
- Inference Specialization: Mastery of core inference optimization techniques, including multi-level KV Cache management, Quantization, and Intelligent Routing.
- Systems Proficiency: Expert in parallel computing and distributed systems; deep understanding of low-level programming models (e.g., CUDA, Triton) and inference engine architectures.
- Frameworks & Models: Familiar with mainstream deep learning frameworks (e.g., PyTorch, TensorFlow); experience in optimizing ultra-large-scale models is highly preferred.
- Industry Insight: Stay current with global evolutions in inference technology and computing architectures, with the ability to objectively evaluate different technical paths.
- Professional Skills: Strong analytical and cross-team collaboration skills, with a proven track record of leading complex inference projects to fruition.
- Preferred Qualifications: 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.
Location State(s)
US-California-Palo Alto
The expected base pay range for this position in the location(s) listed above is $120,100.00 to $225,700.00 per year. Actual pay may vary depending on job-related knowledge, skills, and experience.Employees hired for this position may be eligible for a sign on payment, relocation package, and restricted stock units, which will be evaluated on a case-by-case basis.Subject to the terms and conditions of the plans in effect, hired applicants are also eligible for medical, dental, vision, life and disability benefits, and participation in the Company's 401(k) plan. The Employee is also eligible for up to 15 to 25 days of vacation per year (depending on the employee's tenure), up to 13 days of holidays throughout the calendar year, and up to 10 days of paid sick leave per year.Your benefits may be adjusted to reflect your location, employment status, duration of employment with the company, and position level. Benefits may also be pro-rated for those who start working during the calendar year.
Equal Employment Opportunity at TencentAs an equal opportunity employer, we firmly believe that diverse voices fuel our innovation and allow us to better serve our users and the community. We foster an environment where every employee of Tencent feels supported and inspired to achieve individual and common goals.
Never miss an opportunity! Create an alert based on the job you applied for.
