Workloads Engineer

Workloads Engineer

(AI Systems / HW-SW Optimization)

Role Overview

This is not a traditional software engineering role.

We are looking for a Workloads Engineer responsible for translating AI models into efficient, production-ready execution on a new hardware + software stack. The role sits at the intersection of AI model understanding, systems engineering, and low-level performance optimization.

You will work across the full stack — from AI model structure down to hardware execution, ensuring that workloads are efficient, scalable, accurate, and robust on next-generation compute platforms.

Key Responsibilities

• Analyze AI model architectures (including LLMs) and translate them into optimized execution workloads for custom HW/SW platforms

• Design and implement high-performance software components for AI frameworks and runtime environments

• Optimize AI workloads for:

  • performance (latency / throughput)

  • memory efficiency

  • parallel execution

  • numerical accuracy and stability

• Identify and remove performance bottlenecks across the stack (model → runtime → hardware)

• Contribute to design decisions for AI execution stack and system architecture

• Support deployment and scaling of AI workloads in real-world environments

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Mathematics, Engineering, or related field

• 5+ years of hands-on software engineering experience (or AI model development experience)

• Strong programming skills in Python and C++

• Strong algorithmic thinking and ability to solve complex computational problems

• Solid understanding of AI model architectures, especially transformers and LLMs

• Experience in performance optimization (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 (latency, throughput, memory footprint optimization)

• Familiarity with distributed systems and model deployment pipelines

• Understanding of computer architecture (CPUs, GPUs, accelerators, memory hierarchies)

• Experience working close to hardware / compilers / runtime systems

What We Offer

• Highly competitive salary, employment contract (Umowa o Pracę), and a comprehensive benefits package, including Medicover healthcare coverage.

• Work on the performance-critical compute layer for next-generation AI accelerators

• Direct impact on deep learning model efficiency and latency

• Collaboration with experts in hardware, compilers, and systems

• Challenging low-level performance engineering problems at the hardware–software boundary