Senior Distributed ML Engineer at European Tech Recruit

Berlin, Berlin, Germany -

Full Time

Start Date

Immediate

Expiry Date

30 Dec, 26

Salary

80000.0

Posted On

01 Oct, 26

Experience

20 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Industry

Information Technology & Services

Description

Infrastructure & Model Acceleration:Collaborate with ML researchers and engineers to accelerate model development, training, and inference across distributed computing environments.
Build, scale, and improve the reliability and efficiency of distributed training and inference workflows.

System Optimization & Bottleneck Diagnosis:Investigate performance bottlenecks across distributed ML workloads.
Profile code and optimise the utilization of GPU, CPU, memory, and networking resources.

Tooling & Best Practices:Develop tools, libraries, and orchestration infrastructure to make distributed computing resources accessible.
Establish, document, and maintain engineering best practices across technical teams while evaluating emerging technologies.

Requirements:Degree in Computer Science, Computer Engineering, Software Engineering, or a related field (Master's or PhD preferred).
3+ years of experience designing and implementing distributed machine learning training systems or frameworks.
Hands-on experience with technologies like Megatron, DeepSpeed, Hugging Face Accelerate, FSDP, vLLM, or verl.
Experience with cloud platforms (AWS, GCP, Azure), workload managers (Ray, SLURM), containerization (Docker, Kubernetes), gRPC, and vector databases.
Proven track record in GPU performance profiling and optimization using tools like PyTorch Profiler, PyProf, or NVIDIA Nsight.
Strong problem-solving, debugging, communication, and cross-team collaboration skills.

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Responsibilities

Infrastructure & Model Acceleration:Collaborate with ML researchers and engineers to accelerate model development, training, and inference across distributed computing environments.
Build, scale, and improve the reliability and efficiency of distributed training and inference workflows.

System Optimization & Bottleneck Diagnosis:Investigate performance bottlenecks across distributed ML workloads.
Profile code and optimise the utilization of GPU, CPU, memory, and networking resources.

Tooling & Best Practices:Develop tools, libraries, and orchestration infrastructure to make distributed computing resources accessible.
Establish, document, and maintain engineering best practices across technical teams while evaluating emerging technologies.

Requirements:Degree in Computer Science, Computer Engineering, Software Engineering, or a related field (Master's or PhD preferred).
3+ years of experience designing and implementing distributed machine learning training systems or frameworks.
Hands-on experience with technologies like Megatron, DeepSpeed, Hugging Face Accelerate, FSDP, vLLM, or verl.
Experience with cloud platforms (AWS, GCP, Azure), workload managers (Ray, SLURM), containerization (Docker, Kubernetes), gRPC, and vector databases.
Proven track record in GPU performance profiling and optimization using tools like PyTorch Profiler, PyProf, or NVIDIA Nsight.
Strong problem-solving, debugging, communication, and cross-team collaboration skills.