Opportunity at
NebiusML Infrastructure Engineer
Nebius, a Nasdaq-listed company headquartered in Amsterdam with an international team exceeding 1,500 employees, is currently hiring an ML Infrastructure Engineer for an on-site position. The…
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About This Role
Nebius, a Nasdaq-listed company headquartered in Amsterdam with an international team exceeding 1,500 employees, is currently hiring an ML Infrastructure Engineer for an on-site position. The organization operates a full-stack cloud platform designed for the global artificial intelligence economy, supporting developers and businesses throughout data training and deployment stages. In this role, the selected professional will lead the evaluation and assessment of GPU platforms dedicated to machine learning and artificial…
Job Description
Nebius, a Nasdaq-listed company headquartered in Amsterdam with an international team exceeding 1,500 employees, is currently hiring an ML Infrastructure Engineer for an on-site position. The organization operates a full-stack cloud platform designed for the global artificial intelligence economy, supporting developers and businesses throughout data training and deployment stages.
In this role, the selected professional will lead the evaluation and assessment of GPU platforms dedicated to machine learning and artificial intelligence workloads. Responsibilities include analyzing hardware efficiency, reviewing system architectures, and running tests on newly deployed GPU clusters. This position suits individuals with a strong background in deep learning performance, neural network training, and GPU software stacks who want to contribute to next-generation hardware development and platform optimization.
Responsibilities
- Profile and analyze GPU performance at the system and kernel level alongside hardware and development teams
- Compare GPU efficiency across various architectures, software stacks, and platforms
- Identify and resolve performance bottlenecks by debugging machine learning workloads
- Conduct acceptance testing for new GPU clusters to verify stability, compatibility, and performance for artificial intelligence workloads
- Run experiments across different system configurations to measure the effects of interconnect strategies and optimizations
- Build tools and dashboards to track performance trends and metrics
- Contribute to internal frameworks, tooling, and best practices
Requirements
- Profound understanding of machine learning theoretical foundations
- Deep knowledge of performance aspects related to large neural network inference and training
- Extensive experience utilizing modern deep learning frameworks
- Good comprehension of the GPU stack including CUDA, drivers, NCCL, and related libraries
- Familiarity with containerized environments such as Kubernetes and Docker
- Strong communication skills and the ability to operate independently
- Authorization to work in the country of application with proof of eligibility required
Qualifications
- Familiarity with modern large language model inference frameworks
- Experience working with Python and performance profiling tools
- Familiarity with cloud machine learning platforms like Azure ML, GCP, and AWS
- Prior contributions to open-source machine learning benchmarking tools
Core Skills
Benefits
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this position remote or on-site?
The posting explicitly designates this role as on-site.
What is the salary for the ML Infrastructure Engineer role?
The job posting does not specify the salary, mentioning only that compensation is competitive.
What qualifications are required to apply?
Applicants must have a deep understanding of machine learning foundations, experience with modern deep learning frameworks, familiarity with containerized environments, and a good grasp of the GPU stack including CUDA and drivers.
Sample Interview Questions
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