Wayve
Tech Lead / Manager - Trajectory Generation and Controls
Sunnyvale, California USA · Full-time
Sponsorship not specified$276k-$311kDetected 121 days ago
PythonC++Machine LearningRoboticsLeadership
Stay score
odds of building a lasting career here
61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
12 transfer filings in the last year, covering 12 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- You will guide a team working across controls, trajectory generation, and the interface with the Wayve AI Driver.
- The role combines technical leadership, team development, and delivery ownership, with the opportunity to shape both near-term product impact and the longer-term evolution of our technology.
Responsibilities
- Lead and develop the Motion Controls team, providing strong people leadership, technical direction, and clear priorities for an experienced and growing group of engineers.
- Own the team's technical roadmap and delivery across short and long term horizons, translating product and system needs into clear priorities, technical plans, and timely execution.
- Own the interface between the Wayve AI Driver, trajectory generation, and control in a deployed autonomy system.
- Provide strong technical judgment across control, trajectory generation, and adjacent ML-informed system design, holding a high bar for engineering quality, technical rigor, deployed system performance, and motion quality.
- Work with key partners to align technical plans, product needs, and integration constraints in the delivery of robust deployed motion behavior.
- Experience building, shipping, and maintaining production systems, including translating product and system requirements into technical plans and execution.
Requirements
- Expertise in controls, trajectory generation, motion planning, or closely related autonomy problems, including strong understanding of physical system dynamics, closed-loop behavior, and controller design tradeoffs.
- Experience with classical search- or optimization-based motion planning, optimal control, MPC, or related motion-generation methods.
- Experience with automotive-grade software development processes or safety-related standards such as ISO26262 or SOTIF.
- Experience with Python and C++ in production autonomy or robotics systems.
Compensation
- This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated salary for this role ranges from $276,100 to $311,400, plus a competitive equity package.
Benefits
- Experience working on production systems that incorporate machine learning models, ideally in domains such as prediction, trajectory generation, planning, or control.
Company info
- ABOUT YOU In order to set you up for success as a TLM, Motion Controls at Wayve, we're looking for the following skills and experience.
- At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.
- In order to set you up for success as a TLM, Motion Controls at Wayve, we're looking for the following skills and experience.
This listing is sourced directly from Wayve's careers page and normalized into a canonical job model.