About the job
We are seeking talented Software Engineers to join our Model Infrastructure team. You will focus on the architecture and engineering challenges associated with large-scale recommendation models – from online inference to offline training. Your work will directly contribute to supporting increasingly complex models, solving bottlenecks in computation and storage, and enabling breakthrough innovations in recommendation algorithms.
Responsibilities
- Build and optimize infrastructure for online inference and offline training of recommendation models.
- Solve challenges posed by the combination of high model complexity, massive data, and large-scale deployments.
- Work closely with algorithm researchers to co-design infrastructure aligned with cutting-edge machine learning frameworks and hardware accelerators.
- Continuously improve model architecture, efficiency, and scalability to enable better user experiences.
- Abstract and build reusable infrastructure components and tools that benefit the broader recommendation stack.
Qualifications
Minimum
- Bachelor's degree or above, majoring in Computer Science, or related fields.
- Understanding of GPU architecture and the software stack (CUDA, CUTLASS, Triton Lang), with experience in GPU performance analysis.
- Experience with at least one machine learning framework (e.g., TensorFlow, PyTorch, or in-house alternatives).
- Initiative and self-drive; eager to take on complex challenges.
- Proven ability to work independently and collaboratively in a fast-paced team environment.
Preferred
- Familiarity with implementation details of deep learning networks and low-level operators.
- Experience with large-scale distributed model training.
- Knowledge of DNN compilers such as MLIR, XLA, or TVM.
- Ability to analyze and optimize training/inference algorithms using mathematical tools.