Workloads Engineer

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SeniorOn-site·GdańskFull-time·Umowa o pracę

#411205·Dodano dwa miesiące temu·7

Źródło: EER Poland

Tech Stack / Keywords

AIPython PyTorch TensorFlowCUDAOpenCLArchitecture

EER Poland is the hiring organization for this role located in Gdansk, Poland.

Bachelor’s or Master’s degree in Computer Science, Mathematics, Engineering, or related field
5+ years of hands-on software engineering or AI model development experience
Strong programming skills in Python and C++
Strong algorithmic thinking and problem-solving ability
Solid understanding of AI model architectures, especially transformers and LLMs
Experience in performance optimization including compute, memory, and parallelization techniques
Strong communication skills and ability to work in cross-functional teams

Nice to have:

Experience with AI frameworks such as PyTorch, JAX, TensorFlow (training or inference)
GPU programming experience (CUDA, OpenCL) or parallel computing systems
Experience with AI performance tuning for latency, throughput, and memory footprint
Familiarity with distributed systems and model deployment pipelines
Understanding of computer architecture including CPUs, GPUs, accelerators, and memory hierarchies
Experience working close to hardware, compilers, and runtime systems

Analyze AI model architectures including LLMs and translate them into optimized execution workloads for custom hardware and software platforms
Design and implement high-performance software components for AI frameworks and runtime environments
Optimize AI workloads for latency, throughput, memory efficiency, parallel execution, numerical accuracy, and stability
Identify and remove performance bottlenecks across the stack (model to runtime to hardware)
Contribute to design decisions for AI execution stack and system architecture
Support deployment and scaling of AI workloads in real-world environments

Highly competitive salary with an employment contract (Umowa o Pracę)
Comprehensive benefits package including Medicover private healthcare
Opportunity to work on performance-critical compute layers for next-generation AI accelerators
Direct impact on deep learning model efficiency and latency
Collaboration with experts in hardware, compilers, and systems
Challenges involving low-level performance engineering at the hardware–software boundary

Opieka zdrowotna

EER Poland

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