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Optical Components Engineer

Job in Sunnyvale, Santa Clara County, California, 94087, USA
Listing for: Rippling, Inc.
Full Time position
Listed on 2026-10-13
Job specializations:
  • Engineering
    Quality Engineering, Test Engineer, Mechanical Engineer
Salary/Wage Range or Industry Benchmark: 150000 - 230000 USD Yearly USD 150000.00 230000.00 YEAR
Job Description & How to Apply Below
  • Lyte is scaling 4D FMCW lidar into volume production. This role owns the precision optical components that go into our sensors - lenses, prisms, and other optic elements - from first specification through vendor qualification to steady, on-time volume supply. You will be the single technical owner for each component and its vendors. You will drive component specifications to convergence across optical design, systems, mechanical, and the vendor;

    tie every component tolerance back to system-level lidar performance; hold vendors accountable for delivery; define how parts are tested both at the vendor and in house; and own component reliability through qualification and ongoing monitoring. Underpinning all of it is optical simulation: you will build the modeling capability that predicts how component variation shows up at the system level, and use it to set specs that are both sufficient and manufacturable.
What

you'll do
  • Own the optical component supply base
    - Identify, assess, qualify, and manage vendors for lenses, prisms, windows, wave plates, and coatings. Run technical reviews and capability assessments, and lead second-source qualification in partnership with procurement and quality.
  • Drive component spec convergence
    - Take each component from first draft to signed-off drawing — surface form and irregularity, transmitted wavefront error, centration and wedge, dimensional and angular tolerances, coating reflectance and transmittance, laser damage threshold, scratch-dig, and environmental requirements. Align optical design, mechanical, systems, and vendor manufacturability, close open items, and freeze specs on schedule.
  • Correlate component specs to system specs
    - Build and maintain the flow-down from system-level 4D FMCW lidar performance - range accuracy and precision, SNR, signal intensity, and point-cloud quality — to component tolerances. Use sensitivity and tolerance analysis to show which parameters truly matter and where margin can be released to improve cost and yield.
  • Drive vendor delivery
    - Own component build plans and delivery commitments across prototype, EVT/DVT/PVT, and volume ramp. Track WIP, yield, and capacity at each vendor, flag risks early, and drive recovery plans with procurement and NPI program management.
  • Test at the vendor
    - Define vendor outgoing inspection and test plans — measurement methods, sampling, and data reporting formats. Audit vendor metrology and ensure vendor data is Cp/Cpk-capable and delivered with every shipment.
  • Test in house
    - Define incoming inspection and engineering characterization at Lyte — interferometry, MTF, spectral transmittance and reflectance, polarization, and dimensional metrology. Partner with test engineering on IQC stations, maintain golden samples, and prove vendor-to-Lyte measurement correlation.
  • Own component reliability
    - Define and own reliability qualification for every optical component — environmental and lifetime test plans covering thermal cycling and shock, damp heat and humidity, vibration and mechanical shock, coating adhesion and durability, laser damage, and UV and solar exposure, aligned to automotive and lidar product requirements. Execute tests at supplier sites, in Lyte's internal labs, or at external test labs as best fits each test, and own the results end to end: pass/fail disposition, failure analysis, corrective action with vendors, and ongoing reliability monitoring through production.
  • Build optical simulation capability
    - Model components and sub-assemblies in Zemax Optic Studio or Code V - tolerancing, Monte Carlo, polarization, and stray light and ghost analysis. Compare measured against simulated performance to root-cause shortfalls, and build reusable models and methods the wider team can rely on.
  • Lead…
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