The team's responsibilities include ML and Python infrastructure within the company.

– Building and maintaining pipelines for training models. Key tasks include:

• Training models using deep learning frameworks such as PyTorch and JAX on our cluster;
• Training models using machine learning libraries like CatBoost and XGBoost on our cluster.

– Optimizing model training. Key tasks include:

• Profiling GPU, CPU, memory, and I/O during training to identify bottlenecks and optimization opportunities;
• Debugging performance and quality issues in the training process, such as handling gradient explosions or addressing slowdowns when switching between FP32 and FP16 precision;
• Monitoring and improving GPU utilization on the cluster.

– Building GPU data centers. Key tasks include:

• Comparing and selecting GPUs based on the tasks and training workloads we run.
• Debugging hardware stack issues. For example, addressing multinode DDP training bottlenecks related to interconnects between two GPU hosts.

– At least 3 years of professional experience as a ML/Python/C++ Engineer;

– Strong expertise in databases, data pipelines, and service development;

– Proficiency in Python;

– Experience contributing to projects with complex architecture and high load;

– Linux expertise;

– Expertise in algorithms and data structures;

– Ability to work in a fast-paced environment and efficiently multi-task;

– Ability to communicate effectively within your team and with other teams.

– Experience in developing high load services;

– Experience in SQL/Spark/Pandas/Numpy/PyTorch/Jax;

– Competency in Go/C++;

– Understanding of operating systems, networks, and performance optimization;

– Any experience in competitive programming contests (IOI, ICPC, hashcode) or CTFs.