Own and drive well‑defined projects within our ML platform and training infrastructure
Analyze performance, scalability, and reliability bottlenecks in production ML workflows
Improve observability of training and evaluation pipelines through profiling, logging, and telemetry
Design and integrate MLOps tools that improve developer productivity and system reliability
Develop robust integration tests to improve platform stability
Quantify and validate improvements through systematic benchmarking and experimentation
Document technical designs and findings, and present progress and results to the team

Currently pursuing a BSc, Master’s or PhD in Computer Science, Computer Engineering, or a related field
Expert proficiency in Python and experience with PyTorch or similar ML frameworks
Experience with containerization and deployment technologies (e.g., Docker)
Experience building scalable data processing or ML workflows using systems such as Kubernetes, Airflow, Flyte, or similar platforms
Experience designing, implementing, and maintaining software systems or research tooling
Proficiency with version control systems (e.g., Git)
Familiarity with benchmarking, experimentation, and performance evaluation methodologies
Experience with distributed training frameworks (e.g., PyTorch Distributed, Horovod)
Knowledge of cloud infrastructure and resource management (e.g., AWS, GCP, Azure)
Experience designing ML systems or infrastructure for research or production environments
Background in autonomous driving, robotics, or large‑scale perception systems
Familiarity with C++ or performance‑critical systems programming
Strong technical writing and presentation skills

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Software Engineer, ML Platform – Internship

Key skills