Sponsorship verdict
Sponsorship possible
One solid signal, not two. Worth applying, and worth asking about sponsorship early.
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✓
Employer is on a government sponsor recordThe US Department of Labor certified 5 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
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The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
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No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
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✓
What Glydways INC paid sponsored hires in similar roles1 certified filing for “Localization Engineer” (Robotics Engineers) in CA: $280k–$280k, median $280k.
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✓
Confirmed live todayWhen a source last listed this job as open.
Details and sources
- Employer is on a government sponsor record
- The US Department of Labor certified 5 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
- No salary bar for this route
- H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Glydways INC paid sponsored hires in similar roles
- 1 certified filing for “Localization Engineer” (Robotics Engineers) in CA: $280k–$280k, median $280k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.
About the role
Who we are:
Glydways is reimagining what public transit can be. We believe that mobility is the gateway to opportunity—connecting people to housing, education, employment, commerce, and care. By making transportation more accessible, affordable, and sustainable, we empower communities to thrive and unlock economic and social prosperity.
Our mission is to revolutionize transit with a solution that delivers high capacity, exceptional user experiences, unmatched affordability, and minimal environmental impact.
The Glydways system is a groundbreaking network of carbon-neutral, interconnected transit pathways powered by standardized autonomous vehicles on dedicated roadways. Operating 24/7 with on-demand access, it offers personalized and efficient mobility—without the burden of heavy upfront infrastructure costs or ongoing taxpayer subsidies.
With Glydways, we’re building more than a transportation system; we’re creating a future where everyone, everywhere, has the freedom to move.
Meet the team:
You will join a team that is responsible for developing software for localization, calibration, and mapping of robotic ground vehicles, which we lovingly refer to as Glydcars. The team is composed of team members with strong backgrounds in sensor fusion (LIDAR, RADAR, vision processing, UWB radios, inertial sensors, etc), SLAM (Simultaneous Localization and Mapping), optimization, filtering (Kalman filters, factor graphs, etc) and calibration. Members support development and maintenance of core components of a sophisticated autonomy stack for Glydcars, which operate in a variety of environments including GPS-denied areas. Team members contribute by writing C++ onboard software, peer-reviewing each other’s contributions, developing unit, subsystem, and end-to-end tests, and conducting data analysis (often using Python) in a common software repository.
Roles & Responsibilities:
• Be fluent in the localization team's designs at the source-code level, with the ability to understand and reason about C++, engaging directly with the codebase.
• Own the algorithm and test requirements capture process for the localization team, including conducting requirements reviews and revising requirements with autonomy software, systems, and safety teams.
• Write and own simulation and track test scenarios that validate the localization team's systems, including critical safety functions.
• Write and own Hardware-in-the-Loop (HIL) testing for the localization team.
• Champion strong unit test coverage across the localization team's codebase.
• Pursue systems-level solutions in service of the other developers on the localization team, unblocking algorithm and hardware work.
• Work with neighbor teams such as the safety team to fine-tune localization and safety algorithm configuration settings.
• Work closely with teammates by conducting and receiving code reviews and requirements reviews, demonstrating attention to detail and comprehension of complex systems.
Knowledge, Skills and Abilities:
Required:
• Experience with requirements management tools (e.g., Jama) and structuring test coverage across unit, simulation, track, and HIL (Hardware-In-the-Loop) levels.
• Familiarity with safety qualification standards related to localization accuracy.
• MSc in Mechanical Engineering, Electrical Engineering, Computer Science, or a related systems engineering field. Does not need to be highly senior, but relevant, demonstrable experience is required.
• Hands-on and flexible/adaptable — willing to get into the details and open to changing approach based on feedback.
Desired:
• Strong background in localization algorithms/estimation theory including Kalman filtering and/or pose graph optimization.
• Familiarity with LIDAR- and computer-vision-based loc
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