About this role
Salary Range: PLN 303,800 - 411,000
Subject to alignment to the responsibilities and duties of the role
Requirements
- A passion for your work and the ability to thrive in uncertain and complex environments
- Strong programming skills in Python/C/C++, with a focus on performance-sensitive applications
- Solid understanding of computer architecture, performance profiling, and low-level system behaviour (CPU, memory, I/O)
- Experience with benchmarking and analysing complex, distributed systems
- Familiarity with Linux-based development environments and tools
- Strong problem-solving skills and ability to interpret and communicate performance data clearly
Nice to have
- Knowledge of ML frameworks (ideally PyTorch) and their performance characteristics
- Experience with performance analysis in GPU-accelerated environments (CUDA, ROCm, etc.)
- Familiarity of hardware performance characteristics especially in ML context including high-speed networking (e.g. RoCE, RDMA)
- Familiarity with distributed computing frameworks (ideally collectives experience)
- Experience building dashboards or visualizations for performance monitoring (e.g., Grafana, Prometheus, or custom tooling)
- Exposure to performance regression tracking and CI pipelines for performance validation
About Graphcore
At Graphcore, we’re building the future of AI compute. We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale. As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem. To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world. We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence Job Summary As a Senior Software Engineer in the ML Software Performance Analysis team, you will play a critical role in ensuring end-to-end performance excellence of our proprietary AI hardware and software stack. You will directly report to the Performance Analysis Team Lead and collaborate closely with component teams, including ML Framework developers, Compiler and Runtime teams, Infrastructure engineers, and Product Management. Your work will directly influence the efficiency and scalability of our ML software solutions, significantly impacting our business by enabling reliable and performant AI solutions for customers. The Team The ML Software Performance Analysis team is a part of the wider ML Software Engineering organisation, responsible for delivering optimised, proprietary machine learning solutions. Our team consists of experienced engineers and domain experts focused on rigorous performance benchmarking, in-depth analysis, and cross-layer optimization from single chip to large-scale, distributed systems. We work closely with both internal partners and external collaborators to ensure our solutions meet the highest standards of performance, efficiency, and scalability. Our core responsibilities include: ML Software Stack Performance Reports – We publish regular reports that provide a comprehensive view of the performance status of the ML software stack End-to-End Performance Optimization – We take a holistic approach to performance, ensuring that local optimizations do not lead to global regressions. Our work spans component boundaries, enabling balanced and efficient performance across the entire stack Responsibilities and Duties Conduct in-depth analysis of performance metrics to identify bottlenecks, inefficiencies, and regression trends across the ML stack Collaborate with cross-functional teams to drive end-to-end performance improvements across software components Prepare and deliver performance reports, summarizing key findings, trends, and recommendations Design, implement, and maintain performance benchmarking tools and infrastructure for large-scale ML software systems Investigate and resolve performance-related issues, including CPU utilization, memory usage, and network overhead Ensure that local optimizations do not negatively impact overall system performance, applying a global performance perspective Provide actionable feedback and guidance to engineering teams to support continuous performance optimization Candidate Profile