NEW JOB OPENING
SENIOR MACHINE LEARNING ENGINEER, COMPUTER VISION & AR DISPLAY SYSTEMS
IN Redmond, WA, USA!

Location: Onsite - Redmond, WA or Sunnyvale, CA



Degrees/Certifications Required:

• M.S. or Ph.D. in EE, CS, Optical Eng, Applied Physics, or related engineering field



Must-Have Skills:

• Python + PyTorch for ML/AI algorithm development

• Image processing / computer vision (feature detection, segmentation, classification, pattern matching)

• Building & maintaining data processing pipelines for large-scale experimental/manufacturing data



Nice-to-have Skills:

• Display metrology (MTF, luminance uniformity, chromaticity, contrast) and sensor characterization (SNR, noise modeling)

• Deep learning for defect/anomaly detection in manufacturing

• AR/VR display tech (waveguides, micro-LEDs, LCoS, HOEs) + viz tools (Plotly/Matplotlib/Tableau/Unidash)

• Past Client experience is a nice to have



Years of Experience:

• 5+ preferred - definitely within the scope of requirements mentioned above



About the Role:

• The Display Systems Engineering Team within Client Hardware org is responsible for display integration, characterization, and system-level performance for AR glasses products. Our team develops and qualifies display modules, and metrology systems that define the visual quality of next-generation AR experiences.

• We are seeking an ML/AI Engineer (Contingent Worker) to own the data processing pipeline and develop algorithms for identifying visual artifacts and display performance issues across our product programs. This role sits at the intersection of machine learning, image/signal processing, and display hardware — enabling data-driven decisions that improve display quality, yield, and reliability.



Responsibilities

• Own end-to-end data processing pipelines for display system characterization data (sensor images, metrology measurements, yield data) across multiple product builds Develop ML/AI algorithms to automatically identify and classify visual artifacts, display defects, and performance anomalies in sensor and camera data Build automated analysis tools for disparity sensor performance evaluation, including SNR estimation, pattern detection accuracy, and ambient cross-talk assessment Design and implement anomaly detection models to flag display performance regressions in manufacturing and integration test data Create data visualization dashboards and reporting tools to communicate display quality metrics to cross-functional hardware teams Develop image processing algorithms for waveguide characterization — including uniformity analysis, efficiency mapping, and defect detection Collaborate with optical, process, and integration engineers to translate hardware requirements into algorithmic solutions and validate model performance against ground truth Maintain and improve data infrastructure (collection, storage, versioning, and access) supporting the team's ML and analytics workflows Document methodologies and contribute to team knowledge base for reproducible analysis



Responsibilities:

• Own end-to-end data processing pipelines for display system characterization data (sensor images, metrology measurements, yield data) across multiple product builds

• Develop ML/AI algorithms to automatically identify and classify visual artifacts, display defects, and performance anomalies in sensor and camera data

• Build automated analysis tools for disparity sensor performance evaluation, including SNR estimation, pattern detection accuracy, and ambient cross-talk assessment

• Design and implement anomaly detection models to flag display performance regressions in manufacturing and integration test data

• Create data visualization dashboards and reporting tools to communicate display quality metrics to cross-functional hardware teams

• Develop image processing algorithms for waveguide characterization — including uniformity analysis, efficiency mapping, and defect detection

• Collaborate with optical, process, and integration engineers to translate hardware requirements into algorithmic solutions and validate model performance against ground truth

• Maintain and improve data infrastructure (collection, storage, versioning, and access) supporting the team's ML and analytics workflows

• Document methodologies and contribute to team knowledge base for reproducible analysis



Minimum Qualifications:

• M.S. or Ph.D. in Electrical Engineering, Computer Science, Optical Engineering, Applied Physics, or a related quantitative field

• 3+ years of experience in ML/AI algorithm development for image processing, signal processing, or sensor data analysis

• Strong proficiency in Python and experience with ML frameworks (PyTorch)

• Experience with image processing and computer vision techniques (feature detection, segmentation, classification, pattern matching)

• Demonstrated ability to build and maintain data processing pipelines for large-scale experimental or manufacturing data

• Experience with statistical analysis, hypothesis testing, and experimental design

• Strong problem-solving skills with ability to work through ambiguous, hardware-related technical challenges

• Excellent communication skills — ability to present data-driven findings to cross-functional engineering teams



Preferred Qualifications:

• 5+ years of relevant industry experience in optics, display systems, or semiconductor/hardware characterization

• Experience with display metrology — MTF, luminance uniformity, chromaticity, contrast measurements

• Familiarity with optical system modeling and ray-tracing concepts (Zemax, Code V, or equivalent)

• Experience with deep learning for defect detection or anomaly classification in manufacturing contexts

• Knowledge of AR/VR display technologies — waveguides, micro-LEDs, LCoS, holographic optical elements

• Experience with sensor characterization — SNR analysis, noise modeling, dynamic range assessment

• Proficiency with data visualization tools (Plotly, Matplotlib, Tableau, or Unidash)

• Experience with version control (Git), collaborative development environments, and CI/CD pipelines

• Familiarity with Client's internal tools and data infrastructure is a plus





Notice:

Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Los Angeles Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, qualified applicants will be considered for assignment with arrest and conviction records. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness, meet client expectations, standards, and accompanying requirements, and safeguard business operations and company reputation.

• **Only those lawfully authorized to work in the designated country associated with the position will be considered.**

• **Please note that all Position start dates and duration are estimates and may be reduced or lengthened based upon a client’s business needs and requirements.**