Product & Yield Engineer (Data-focused)

About Fractile

Fractile was founded in 2022 on the bet that, eventually, the world’s most capable AI systems would be limited in their impact by the time taken to produce useful outputs. We bet everything on the logical conclusion: that the only way to truly unlock this latent value, to make speed viable at scale, was to radically re-invent the hardware that we run our frontier AI models on. Ever since, we have been building chips and systems that tackle this problem: how to efficiently generate output at thousands of tokens per second, while handling the complexity and capacity challenges of operating large models at very long contexts.

The workloads that push to the limits of the current frontier are already transformational; it is the technical and economic limits on inference speed that are constraining progress. The defining work of the 21st century will be marked by the engine of inference delivering immense and diffuse chains of intellectual inquiry, in drug discovery, in software engineering, in materials discovery, in any field where progress is driven by deep reasoning and intelligence to resolve complex problems.

We are looking for an Engineer to play a key role in the development and manufacture of Fractile's silicon products, with a particular focus on product, yield and manufacturing data analysis. Working across New Product Introduction (NPI), Yield Engineering and Product Quality, you will analyse large and complex semiconductor datasets to identify trends, isolate sources of variation, improve yield and reliability, and provide clear technical insight to engineering teams and manufacturing partners. This is a highly hands-on individual contributor role for someone who enjoys working directly with data, investigating complex technical problems and turning analysis into actionable engineering conclusions.

Skills and Experience

• Demonstrable experience within the semiconductor industry in Product Engineering, Test Engineering, Yield Engineering, Data Analytics or Quality Engineering, ideally spanning NPI through to high-volume production
• Strong experience analysing semiconductor manufacturing and test data to identify trends, correlations, excursions, yield loss mechanisms and opportunities for improvement
• Solid experience in as many of the following areas as possible:
  • Wafer fab: analysis of Process Integration, Yield Enhancement, D0 Control, defect reduction and process variation data
  • IC Assembly: Assembly Process or Package Design, 3D IC and wafer stacking, Assembly Quality Control, BOM optimisation and Yield Engineering
  • Fabless Semiconductor: IC Product Qualification, reliability and failure analysis, RMA investigation and production monitoring
• Strong knowledge of WAT, WS/CP and FT datasets, including correlation across manufacturing and test stages
• Excellent data mining, statistical analysis and root-cause investigation skills, with the ability to turn complex datasets into clear engineering conclusions
• Experience identifying yield signatures, parameter distributions, outliers, spatial patterns, lot-to-lot variation and systematic versus random failure mechanisms
• Extensive hands-on use of semiconductor industry data analysis tools and software such as yieldHUB, Spotfire, JMP or equivalent
• Ability to create scripts, automated analyses, dashboards and repeatable analytical workflows for standard engineering investigations and reporting
• Experience applying statistical methods to semiconductor manufacturing data, including SPC, Cp/Cpk, distributions, correlation analysis, hypothesis testing and trend analysis
• Ability to combine data from multiple sources and manufacturing stages to investigate complex product and process issues
• Familiarity with FMEA-derived semiconductor qualification plans and relevant standards such as JEDEC
• Understanding of semiconductor failure analysis methods and the ability to use analytical data to guide selection of destructive and non-destructive investigation techniques
• Strong ability to communicate analytical findings through concise visualisations, reports and technical presentations
• Comfortable working independently on ambiguous technical problems and driving investigations from initial data exploration through to a supported conclusion
• Good communication skills and a collaborative, team-player attitude
• Self-starter, flexible and able to manage multiple analyses and investigations in parallel
• Willing and able to travel worldwide when needed