Machine Learning/Artificial Intelligence Engineer - Level III

ML/AI Engineer Display Systems Engineering Team | RL-Hardware

About the Team & Role

The Display Systems Engineering Team within RL Hardware 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 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.

You ll sit at the intersection of ML, image/signal processing, and display hardware; enables data-driven display quality, yield, and reliability decisions.

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

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

Minimum Qualifications

• 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 internal tools and data infrastructure is a plus

What makes this role interesting? Direct impact on the visual quality of next-gen AR glasses; hands-on with cutting-edge display metrology and prototypes; ML applied to real hardware data.

Unique Selling Points:

Rare intersection of ML + optics/display hardware at RL scale; access to unique metrology data and prototypes.

exposure to AR display tech (waveguides, micro-LEDs, LCoS), metrology, and data infrastructure.

Candidate Disqualifiers: Pure software/web engineers with no image/signal/sensor data experience; ML generalists with no scientific/experimental-data background.

Immediate Disqualifier: No Python/PyTorch; no demonstrated pipeline work; unable to handle ambiguous hardware-related problems.

Difficult Aspects of Job: Ambiguous, hardware-driven problems with noisy/imperfect data; ground-truth validation against physical measurements; cross-functional dependencies with optical/process/integration engineers; ramp-up on display metrology and internal tooling.

Interview Process: Hiring-manager + team member (FTE) 1-2 rounds max 1st round - 55 mins; 2nd round - 15-30 mins

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Types of Interviews: Technical (ML/CV + signal/image processing), a coding/data exercise, and a presentation of past relevant work; behavioral for collaboration.