Artur Niederfahrenhorst
Member Of Technical Staff at Anyscale
Cologne, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolArtur Niederfahrenhorst is a Member of Technical Staff at Anyscale with 11 years of engineering experience, focused on robotics and reinforcement learning across EMEA. He is an active RLlib maintainer for the high-profile Ray project, contributing replay buffer API enhancements, prioritized replay fixes, sequence-based replay corrections, and memory usage optimizations. With a technical foundation in electrical engineering (RWTH Aachen, TU Dresden) and computer engineering (Chalmers), he bridges academic research and production ML systems. His career spans research engineering at RWTH Aachen to hands-on software roles at Anyscale, reflecting a pattern of shipping robust, distributed ML infrastructure. Colocated in Cologne, he combines low-level systems thinking with applied RL expertise—often surfacing subtle code-quality and performance improvements that reduce runtime costs.
11 years of coding experience
3 years of employment as a software developer
Elektrotechnik, Elektrotechnik at Technische Universität DresdenMaster of Science - MS Elektrotechnik, Master of Science - MS Elektrotechnik at RWTH Aachen UniversityComputer Engineering, Computer Engineering at Chalmers University of Technology
Github Skills (5)
machine-learning10
python10
reinforcement-learning10
pytorch8
tensorflow8
Programming languages (4)
Github contributions (5)
ray-project/ray
Nov 2021 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 1461 reviews, 175 commits in 1 year 2 months
Contributions summary:Artur's commits focus on modifications to the Replay Buffer API within the RLlib library, including the introduction of new API features, the implementation of replay buffer-related test cases, and addressing memory usage issues. Their contributions encompass enhancements to prioritized replay buffers, fixes to sequence-based replay, and addressing code quality and consistency by reorganizing code. The user also worked on improving the codebase by incorporating various fixes and code improvements.
pythonconsistsruntimetensorflowserving
ArturNiederfahrenhorst/ray
Jul 2021 - Apr 2025
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Contributions:3 PRs, 2197 pushes, 512 branches in 3 years 9 months
apirayscalabledistributed-applicationshyperparameter
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