Workloads Engineer

Workloads Engineer Workloads Engineer for AI systems: translate models into fast, scalable workloads on next-gen HW/SW; optimize from model to hardware and deploy robust, efficient runtimes. Apply! We usually respond within a day 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 Locations Gdańsk Remote status Hybrid Employment type Full-time