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ML Engineer
(Intern)
(Intern)
We are seeking talented individuals for a ML Engineer position to enhance our data processing and machine learning infrastructure. The team regularly solves challenges related to deep learning, distributed computing, and performance optimization, while our infrastructure is primarily written in Python with potential C++ components.
Software Engineering
fulltime
Requirements
– Strong Python programming skills;
– Experience with machine learning libraries such as PyTorch, pandas, numpy, and Apache Spark;
– Understanding of deep learning training processes, including data preprocessing, model validation, and parameter tuning;
– Ability to analyze and optimize code for performance, including profiling asynchronous code;
– Experience with version control systems, preferably Git;
– Good code culture, including designing maintainable interfaces and conducting thorough code reviews;
– Understanding of parallel and distributed computing;
– Experience with machine learning libraries such as PyTorch, pandas, numpy, and Apache Spark;
– Understanding of deep learning training processes, including data preprocessing, model validation, and parameter tuning;
– Ability to analyze and optimize code for performance, including profiling asynchronous code;
– Experience with version control systems, preferably Git;
– Good code culture, including designing maintainable interfaces and conducting thorough code reviews;
– Understanding of parallel and distributed computing;
– Experience in identifying and resolving bottlenecks in code, with knowledge of performance debugging utilities (e.g., perf, flamegraph, py-spy).
Requirements
– Strong Python programming skills;
– Experience with machine learning libraries such as PyTorch, pandas, numpy, and Apache Spark;
– Understanding of deep learning training processes, including data preprocessing, model validation, and parameter tuning;
– Ability to analyze and optimize code for performance, including profiling asynchronous code;
– Experience with version control systems, preferably Git;
– Good code culture, including designing maintainable interfaces and conducting thorough code reviews;
– Understanding of parallel and distributed computing;
– Experience with machine learning libraries such as PyTorch, pandas, numpy, and Apache Spark;
– Understanding of deep learning training processes, including data preprocessing, model validation, and parameter tuning;
– Ability to analyze and optimize code for performance, including profiling asynchronous code;
– Experience with version control systems, preferably Git;
– Good code culture, including designing maintainable interfaces and conducting thorough code reviews;
– Understanding of parallel and distributed computing;
– Experience in identifying and resolving bottlenecks in code, with knowledge of performance debugging utilities (e.g., perf, flamegraph, py-spy).
Would be a plus
– C++ programming skills;
– Proficiency in Linux environment and command-line utilities (e.g., grep, xargs, less, awk, sed);
– Experience with large and rapidly changing codebases;
– Strong optimizer mindset for proposing improvements and optimizations in data processing, training, and inference code speed;
– First-hand experience with data processing challenges in machine learning contexts;
– Proficiency in Linux environment and command-line utilities (e.g., grep, xargs, less, awk, sed);
– Experience with large and rapidly changing codebases;
– Strong optimizer mindset for proposing improvements and optimizations in data processing, training, and inference code speed;
– First-hand experience with data processing challenges in machine learning contexts;
– Ability to work at the intersection of research and infrastructure development.
Would be a plus
– C++ programming skills;
– Proficiency in Linux environment and command-line utilities (e.g., grep, xargs, less, awk, sed);
– Experience with large and rapidly changing codebases;
– Strong optimizer mindset for proposing improvements and optimizations in data processing, training, and inference code speed;
– First-hand experience with data processing challenges in machine learning contexts;
– Proficiency in Linux environment and command-line utilities (e.g., grep, xargs, less, awk, sed);
– Experience with large and rapidly changing codebases;
– Strong optimizer mindset for proposing improvements and optimizations in data processing, training, and inference code speed;
– First-hand experience with data processing challenges in machine learning contexts;
– Ability to work at the intersection of research and infrastructure development.
Apply
