Machine Learning Engineer - AI Compiler Optimization

Responsibilitie The mission of our AML team is to push the next-generation AI infrastructure and recommendation platform for the ads ranking, search ranking, live & ecom ranking in our company. We also drive substantial impact on core businesses of the company. Currently, we are looking for Machine Learning Engineer in AI Compiler Optimization to join our team to support and advance that mission. Responsibilities: - Responsible for building and implementing the compilation optimization system for the recommendation machine learning engine. Design and implement full-stack optimization solutions at the graph, operator, and memory levels specifically for recommendation model scenarios, including but not limited to graph-operator fusion and automatic operator generation, to maximize hardware computing limits. - Collaborate closely with hardware and algorithm teams to carry out hardware-software co-design. Optimize compilation strategies based on hardware characteristics to improve the efficiency of hardware-software synergy. - Responsible for the compilation adaptation of recommendation models from the PyTorch framework to the engine. Optimize the entire process of model import, conversion, and code generation to simplify the model deployment process and enhance development efficiency. Qualification Minimum Qualifications: - Proficient in one of the mainstream AI compiler frameworks (e.g., Triton, MLIR, TVM), with practical project experience in customized compilation optimization and Pass development based on the framework. - Experience in GPU/NPU compilation optimization, mastering core techniques such as loop optimization, memory optimization, and operator optimization, with the ability to independently perform performance bottleneck analysis and technical optimization. - Familiar with common model structures and compilation adaptation logic of deep learning frameworks such as PyTorch and TensorFlow, capable of designing targeted optimization solutions. Preferred Qualifications: - Familiar with the architecture design of recommendation machine learning engines. Solid implementation experience in compilation optimization and low-latency inference optimization for large-scale recommendation systems, with the ability to handle compilation optimization needs in high-concurrency scenarios. - Experience contributing to open-source AI compiler projects (e.g., TVM, MLIR), or possess technical expertise in the compilation adaptation of large models for recommendation scenarios and the automatic generation of sparse operators. Job Information 【For Pay Transparency】Compensation Description (Annually) The base salary range for this position in the selected city is $162000 - $387600 annually. Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units. Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure). The Company reserves the right to modify or change these benefits programs at any time, with or without notice.