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. Backed by Sequoia.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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.
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...
Browse similar: Startup jobs