- Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure.
- Implement model components, data pipelines, evaluation systems, and numerical methods.
- Build reproducible programmatic workflows using Python and command-line tools.
- Optimize training or inference for latency, throughput, memory usage, and hardware utilization.
- Diagnose numerical instability, incorrect tensor behavior, memory bottlenecks, distributed-system failures, and performance regressions.
Requirements
- A master s degree or PhD in Computer Science, Machine Learning,
Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
- Solid professional or research experience in machine learning.
- Practical proficiency with Python.
- Meaningful experience with at least two relevant ML frameworks, libraries, or inference tools.
- Ability to debug ML systems beyond surface-level API usage.
Application Process
- Easy Apply on LinkedIn
- Check email for next steps
- Participate in resume evaluation & interview stage