Senior Quality Process Development Engineer

Responsibilities:

• Design and build statistical and algorithmic detection tools for wafer probe data — including test hardware integrity checks, software bin/yield outlier detection, parametric drift analysis, and reticle- and wafer-level spatial pattern classification — leveraging Semtech's existing statistical infrastructure (20%)
• Assist on the development of learned pattern-recognition models that classify wafer-level failure patterns from engineer-confirmed examples, complementing the statistical tool set and supporting multi-pattern classification with confidence indicators. Risk-tiering, prioritization, and mitigation-proposal logic — including known-issue catalog matching, lot-level systemic risk reporting, and drafting of wafer-map modification scripts (20%)
• Drive along with the tool software teams the buildup of relationship-analysis and reporting layer that reconciles detection outputs into a consolidated set of distinct issues, including LLM-driven contextualization using product and test/bin naming data, and structured, self-describing output for downstream risk assessment (20%)
• Identify and drive development and requirements for AI process improvement initiatives among the quality engineering teams. (20%)
• Partner with Quality Engineering, Product Engineering, and the AI development team to validate system performance against production fab lots, and produce documentation and knowledge-transfer materials that support long-term, in-house ownership of the system (20%)
• Minimum Qualifications:
• Bachelor's degree in Electrical Engineering, Computer Science, Statistics, or a related field required; Master's or PhD preferred
• 5+ years of professional experience in software engineering, applied statistics, or data science, with a track record of shipping production statistical or machine-learning systems
• Strong foundation in applied statistics, including outlier and anomaly detection, multiple-comparison correction, and hypothesis testing on production or sensor data
• Hands-on experience building and deploying machine learning classification models, including training on labeled data and handling out-of-distribution/novel-category detection
• Proficiency in Python (or equivalent) for data analysis and ML model development, and strong SQL or equivalent skills for working with large structured production datasets
• Demonstrated ability to design structured, self-describing system outputs and to write clear technical documentation for engineering audiences
• Desired Qualifications
• Experience with semiconductor test data, wafer probe, or final test engineering, including familiarity with concepts such as software binning, parametric test limits, and wafer map analysis
• Direct experience with a comparable semiconductor test-data / yield-analysis platform
• Experience building LLM-driven analysis or reporting systems that incorporate domain context to scope and focus model output
• Familiarity with spatial/statistical process control methods (e.g., Tukey-method outlier detection, radial/angular trend analysis, spatial clustering) as applied to manufacturing data
• Prior experience working in a regulated or quality-critical manufacturing environment where system outputs feed formal disposition or non-conforming material review processes
• The intent of this job description is to describe the major duties and responsibilities performed by incumbents of this job. Incumbents may be required to perform job-related tasks other than those specifically included in this description.
• All duties and responsibilities are essential job functions and requirements and are subject to possible modification to reasonably accommodate individuals with disabilities.
• We may leverage Artificial Intelligence (AI) tools to enhance efficiency during candidate screening, assessment, and recruitment. Final hiring decisions remain with our Hiring Teams, not AI systems.
• A reasonable estimate of the pay range for this position is $95,000CAD-$120,000CAD There are several factors taken into consideration in determining base salary, including but not limited to: job-related qualifications, skills, education and experience, as well as job location and the value of other elements of an employee’s total compensation package.