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Semtech

Senior Quality Process Development Engineer

Burlington, ON, Canada

Job type: Full-time
Apply by: Oct 25, 2026
Education: Bachelor’s degree
Experience: 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
Posting language: English
Posted: Sep 25, 2026

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Original job description

Location: Burlington, Canada

Our Team

Semtech's Product Quality Engineering team is responsible for the quality and reliability of every wafer that leaves our fabs and OSAT partners on its way to customers. Our engineers thrive on solving complex technical challenges and driving improvements that directly impact product reliability and customer satisfaction including building a new generation of statistical and data-driven tools to detect, characterize, and triage wafer-level quality issues faster and more consistently than manual review alone can achieve. You'll join a team where mentorship, technical excellence, and knowledge sharing create a culture of growth, innovation, and tangible impact.

Job Summary

The Senior Quality Process Development Engineer will collaborate on the design, development, and validation the statistical and machine-learning systems at the core of Semtech's quality processes along with our tool software partners. This role will define the technical development specifications for quality process improvement activities; including detection and pattern-recognition capabilities that distinguish genuine product and process issues from test-induced artifacts, and that support downstream risk assessment and mitigation for production wafer lots. The engineer will work closely with our tool development partners, internal AI team, Quality Engineering, Product Engineering, and the in-house wafer assessment tool development team to integrate these capabilities into existing production systems and will build learned pattern-recognition models from engineer-confirmed labels to complement rules-based statistical detection.

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.

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More about this job

Responsibilities

Develop and validate statistical and machine-learning systems for detecting, classifying, and assessing risks related to wafer-level quality issues. Collaborate with engineering and software teams to integrate these capabilities into production systems, validate performance, and provide documentation for long-term internal ownership.

Requirements

A bachelor's degree in Electrical Engineering, Computer Science, Statistics, or a related field is required; a master's degree or PhD is preferred. Candidates need at least five years of relevant experience, strong applied statistics and machine-learning skills, proficiency in Python and SQL, and the ability to produce clear technical documentation.

Skills

Applied Statistics
Anomaly Detection
Machine Learning
Classification Models
Python
SQL
Wafer Probe Data Analysis
Semiconductor Test Engineering
Yield Analysis
Spatial Pattern Classification
Statistical Process Control
Large Language Models
Risk Assessment
Technical Documentation
Production Data Analysis

Visa sponsorship

Not detected in the job text

Keywords

Semiconductor Manufacturing
Product Quality Engineering
Wafer Probe Data
Statistical Analysis
Anomaly Detection
Hypothesis Testing
Machine Learning
Classification Models
Out-of-Distribution Detection
Python
SQL
Software Binning
Parametric Test Limits
Wafer Map Analysis
Yield Analysis
Spatial Clustering
Radial and Angular Trend Analysis
Statistical Process Control
Large Language Models
Risk Tiering
Lot-Level Risk Reporting
Production Data
Quality-Critical Manufacturing
Technical Documentation

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