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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.
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
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
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London, GBR
London, GBR
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Bristol, GBR
London, GBR
London, GBR
London, GBR
London, GBR
Bristol, GBR
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
Experience building automated semiconductor yield-monitoring, reporting or anomaly-detection workflows
Experience working with large semiconductor datasets using SQL, Python or similar data-analysis tools
Experience developing dashboards, standard analytical journals or reusable engineering analysis frameworks
Multi-cultural awareness, including experience working with Far East suppliers and manufacturing partners
A Bachelor's degree or above in Electronics, Microelectronics, Semiconductor Physics, Engineering or a similar discipline
Our work involves technologies subject to UK, US and other international export control regulations. Certain roles may require additional eligibility checks to ensure compliance with applicable law. We'll be transparent about this throughout the hiring process.
UK private semiconductor startup building AI inference chips and systems for companies running frontier models.
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