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Added 2 hours ago
Designer, Development (Service)
Husky Technologies
Bolton, ON
Full-time
The service team is currently seeking a Mechanical engineer/designer to specialize in resolving service issues.
Added 2 hours ago
ERP Business Analyst
Husky Technologies
Bolton, ON
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Develop test plans, coordinate testing activities, execute system validation, and support business users during User Acceptance Testing (UAT).
Added 2 hours ago
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At Synopsys, you will work on memory interface IP that powers real customer products, and your validation work will determine whether that IP ships on time and works in the field.
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Added 6 hours ago
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The Senior Data Engineer provides architectural direction, technical leadership, and execution oversight for our core database modernization initiative. This role is responsible for shaping…
Added 8 hours ago
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Develop test plans, coordinate testing activities, execute system validation, and support business users during User Acceptance Testing (UAT).
Added 8 hours ago
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Husky Technologies
Bolton, ON
Full-time
The service team is currently seeking a Mechanical engineer/designer to specialize in resolving service issues.
Added 8 hours ago
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Lead end-to-end innovation and engineering initiatives, from concept ideation and prototyping through proof-of-concept validation, product development, and commercialization.
Added 8 hours ago
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Added 10 hours ago
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Permanent North American Gaeltacht, ON
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Added 13 hours ago
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Added 14 hours ago
Product Development Engineer|
Husky Technologies
Bolton, ON
Full-time
Our Development Engineering team is looking to hire an experienced Product Development Engineer. The successful candidate will lead the technical development of packaging products working…
Added 14 hours ago
Applications Engineer
Husky Technologies
Bolton, ON
Full-time
Lead end-to-end innovation and engineering initiatives, from concept ideation and prototyping through proof-of-concept validation, product development, and commercialization.
Added 14 hours ago
ERP Business Analyst
Husky Technologies
Bolton, ON
Full-time
Develop test plans, coordinate testing activities, execute system validation, and support business users during User Acceptance Testing (UAT).
Added 14 hours ago
Designer, Development (Service)
Husky Technologies
Bolton, ON
Full-time
The service team is currently seeking a Mechanical engineer/designer to specialize in resolving service issues.
Added 15 hours ago
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Added 15 hours ago
Senior Quality Process Development Engineer
Semtech
Burlington, ON · CAN - Burlington, ON
Full-time
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…
Added 15 hours ago
Senior NPI Product Engineer
Semtech
Burlington, ON · CAN - Burlington, ON
Full-time
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Lead V&V Engineer
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8+ years of Verification & Validation experience.
Added 22 hours ago
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Semtech

Senior Quality Process Development Engineer

Burlington, ON, Canada
CAN - Burlington, ON, Canada

Job type: Full-time
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

The Senior Quality Process Development Engineer will design and implement statistical and machine-learning systems to improve wafer-level quality and reliability. They will collaborate with cross-functional teams to integrate these tools into production systems and drive AI-based process improvement initiatives.

Requirements

Candidates must hold at least a bachelor's degree in Electrical Engineering, Computer Science, Statistics, or a related field. A minimum of 5 years of professional experience in software engineering, applied statistics, or data science is required, along with proficiency in Python and SQL.

Skills

Applied statistics
Machine learning
Python
SQL
Data science
Wafer probe
Yield analysis
Anomaly detection
Hypothesis testing
Pattern recognition
Semiconductor testing
Statistical process control
Data analysis
Technical documentation
Risk assessment

Visa sponsorship

Not detected in the job text

Keywords

Semiconductor
Wafer-level quality
Statistical infrastructure
Machine learning models
Outlier detection
Parametric drift analysis
Spatial pattern classification
Risk-tiering
LLM-driven contextualization
Production fab
Test hardware integrity
Software binning
Yield analysis platform
Tukey-method
Radial/angular trend analysis
Spatial clustering
Non-conforming material review
Data-driven tools
Pattern-recognition
Process improvement

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