Staff Engineer, Hardware Development Engineering

WD is building the infrastructure behind the AI-driven data economy.

As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in.

We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.

This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today.

We’re looking for people who want to build, solve, and operate at that level.

Join us and let’s shape the future of data.

We are looking for a motivated and detail-oriented engineer to join our team within the HAMR (Heat-Assisted Magnetic Recording) development organization. This role sits at the intersection of data analysis, cross-functional collaboration, and advanced characterization engineering for next-generation hard disk drive (HDD) recording heads.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

The ideal candidate functions as a data-driven characterization engineer — someone who can analyze complex test data, identify behavioral patterns, compare results across evaluation and control groups, and translate findings into actionable insights for design and process teams.

• Perform day-to-day data analysis on recording head component test results (PTM, QST, CDET, HQST, and related parametrics), comparing evaluation groups against control groups to identify behavioral patterns and anomalies Vs the design’s expectation
• Serve as focal for DOE outgoing shipment reporting across component-level tests (PTM, QST, CDET, HQST, Spin Stand, REC, MLT, IDEC, MI), Identify and flag prone-to-fail samples those tested under typical and/or aggressive conditions to prevent early failures when built into HDDs
• Develop and maintain data stitching/normalization workflows that bring test results onto a common baseline, enabling fair comparison across different test conditions and design variants
• Evaluate the feasibility of using characterization parametric for early-stage projections and conduct regular test result summary to monitor component health trends
• Provide proactive risk identification to enable informed go/no-go decisions before samples leave the site
• Support root cause determination for component-level anomalies (e.g., ADC-related issues) and conduct critical ad-hoc analyses for time-sensitive decision-making on a weekly basis
• Automate recurring analysis and reporting pipelines; adopt AI/ML tools to improve efficiency in pattern recognition and repetitive analysis tasks
• Interface with Great Oak front-end engineers, front-end characterization engineers, wafer design teams, and wafer process teams — translating component data into insights these teams can act on
• Proactively share technical knowledge within the team and across functions — serve as a resource on HAMR head technology, test methodology, and data interpretation

This position is part of our Early Career program at WD. Our Early Career program is designed to support individuals beginning their professional career by providing the foundational training through a structured onboarding, mentorship, and development curriculum.

Required Qualifications

• Currently pursuing or recently completed a PhD’s degree in Electrical Engineering, Materials Science, Physics, Mechatronics , or a related field
• Solid understanding of HDD component testing (heads, sliders, recording performance)
• Proficiency in data analysis using JMP, Minitab, Excel , or equivalent tools
• Ability to work with large datasets, identify trends, and present findings clearly to both technical and non-technical audiences
• Strong communication (both Thai and English) and collaboration skills for cross-functional work across time zones
• A team-player talent, have an enough flexibility and able to handle themselves under pressured conditions.

Skills

• Data Analysis & Statistics
• JMP, Minitab, Excel
• DOE & RCA
• HDD Head Characterization
• Recording Performance Analysis
• Data Normalization & Trend Analysis
• Failure Analysis & Risk Assessment
• Data Visualization & Reporting
• Automation & AI/ML Tools
• Python, SQL, or R
• Power BI or Tableau
• Pattern Recognition & Anomaly Detection
• Critical Thinking & Problem Solving
• Data-Driven Decision Making

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