Machine Learning Engineer - Machine Learning Infrastructure

Machine Learning Engineer - Machine Learning Infrastructure

Location:

San Jose

Team:
Technology

Employment Type:

Regular

Job Code: JLKPV

• About the Team:
  The mission of our AML team is to push next-generation machine learning algorithms and platforms for the recommendation system, ads ranking and search ranking in our company. We also drive substantial impact on core businesses of the company.
• Resource Efficiency Optimization in Distributed Orchestration and Scheduling:
  Develop and extend distributed orchestration frameworks within the Kubernetes/Godel ecosystem. Select appropriate frameworks based on different business scenarios, and optimize cluster utilization and load balancing strategies according to the specific characteristics of each scenario;
  Integrate and expand Auto Scaling and automatic parallelization capabilities for various models and tasks. Employ load modeling and analytic methods for different models to automatically optimize resource requests, achieving large-scale improvements in resource usage efficiency and global optimality;
  Responsible for preemption and re-scheduling mechanisms for services with different priorities, and manage automatic resource multiplexing across different clusters and resource types; handle scheduling and load adaptation across multi-datacenter, multi-region, and multi-cloud environments.
• Building Training System Architecture for Next-Generation Ultra-Large and Ultra-Deep Recommendation Models:
  Develop a flexible, elastic and robust distributed training runtime focused on hyper-scaled embeddings and large-scale GPU training;
  Design and optimize distributed computing APIs and runtimes geared towards future recommendation and ads model paradigms (e.g., reinforcement learning, fine-tuning and/or distillation);
  Collaborate with platform teams to enhance the diagnosability and usability of distributed training systems.
• Constructing Online Orchestration Architecture for Next-Generation Recommendation Systems:
  Build a robust and stable distributed model inference architecture for online learning scenarios involving hyper-scaled embeddings;
  Optimize the usability of online recommendation and ads model architectures and MLops workflows.

Required Qualifications

• Bachelor's degree or above, majoring in Computer Science, Engineering or related fields.
• Strong programming and coding experience with at least one modern language such as Golang, Python
• Experience contributing to the large scale distributed systems, multi-tenant systems (architecture, reliability and scaling)
• Strong analytical abilities and problem solving
• Good communication, self-motivation, engineering practice, documentation, etc.
• At least 3 years of relevant experience.

Preferred Qualifications

• Familiar with large-scale distributed scheduling systems like Kubernetes, Yarn, Flink and/or Spark;
• Familiar with opensourced orchestration frameworks like VeRL, vLLM, Ray or TFX, etc.;

The base salary range for this position in the selected city is $187040 - $438000 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.

For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will…