Director Of Product Engineering

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 Director of Product Engineering, you will be responsible for ensuring that Graphcore products are manufactured with optimised yield, quality and cost, throughout the product lifecycle. To support this, you will recruit and lead a team of Product Engineers to work closely with OSATs, test engineering, manufacturingand quality teams to release new products and sustain high volume production.

The Team

The role will be as part of the Product Test and Diagnosis team, which is part of the Graphcore Manufacturing Operations department and has members in Bristol, Cambridge, India, US and Taiwan. The team is responsible for definition, development, and execution of an end-to-end test strategy spanning silicon, board-level assemblies, server blades, and rack-scale systems. Testing is performed across the full product lifecycle, including manufacturing, and deployed field environments.

Responsibilities and Duties

• Recruit, develop, and manage a team of product engineers with expertise in semiconductor and PCBA manufacturing
• Drive yield, quality and cost optimisation across the product lifecycle, including chip manufacturing, test, PCBA and system level manufacturing

• Work closely with test engineering to define requirements for coverage, reporting and datalog formats from both ATE and system level electrical test
• Own analytics for SMT manufacturing processes, including SPI/AOI/AXI/ICT etc

• Own analytics for board and system level testing, including boundary scan and functional testing at board/blade/rack level
• Work with technology and manufacturing specialists to manage low yield events in production and drive overall yield improvement

• Support RMA analysis and reliability/qualification activities
• Establish best practices for data collection, cleaning, correlation, and visualization

• Collaborate with the Data Engineering and Analytics team to develop loading scripts for new data sources and optimise database schemas
• Define and develop standard analyses such as dashboards and enable fast self-service access to data for end users

• Regular travel to OSATs and manufacturing partners will be required

Candidate Profile

Essential:

• Extensive experience (10+ years) in semiconductor product engineering, test engineering, or manufacturing engineering
• Proven people leadership experience, including cross-geography hiring and team building
  

• Solid experience in high-volume semiconductor/electronics manufacturing, data visualization tools, and manufacturing systems/databases (MES)
• Extensive understanding of semiconductor, PCBA and system level manufacturing and testing

• Extensive expertise in statistical analysis (SPC, DOE, regression, hypothesis testing) and six sigma methodologies
• Extensive expertise in analysing and visualising large manufacturing datasets

• Proficiency in data tools such as Python, SQL, JMP, or similar.
• Demonstrated success driving yield and cost improvements in high-volume production

• Comfortable leading cross-functional teams and communicating clearly and concisely across the organisation
• Degree in Electrical Engineering, Computer Science, Physics or similar

Desirable:

• Extensive experience and expertise in JMP and in using the JMP scripting language to build standard analysis reports, dashboards and applications
• Experience in using data visualisation tools such as Apache Superset&nb