Software Engineer – Motion Planning
DiDi Labs · San Jose, CA
$141,463–$282,264from the description
Jul 10, 2026
San Jose, CA
Jul 21, 2026
What this job asks for AI summary
This role sits within an autonomous vehicle R&D team and focuses on building the decision-making and motion planning stack — from high-level behavioral logic (lane changes, merges, agent interactions) down to path geometry and velocity profile generation. The work involves algorithm design, trajectory optimization, cost-function tuning, and cross-team collaboration with perception, prediction, and control engineers. It suits engineers with a background in robotics or autonomous systems and strong C++ skills.
Mid level · National · Bachelor's required · Full-time
Advertised as Senior, but the requirements read as Mid.
Posted 3 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
What the occupation pays Median $138,970 (middle half $107,524–$175,762). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
The posting advertises two levels simultaneously — Software Engineer and Senior Software Engineer — with separate salary bands ($141,463–$235,182 for SWE; $169,783–$282,264 for Sr. SWE). The comp figures above span both bands. Seniority is assessed as Mid because the qualifications (a B.S./M.S. and unspecified years of experience) do not firmly gate on the Senior level; the Senior title is the higher of the two advertised options.
No specific city or region is stated in the posting; the California privacy notice reference suggests a California-based role, but no CBSA could be confirmed.
Motion planning, trajectory optimization, and world environment reasoning are listed as a grouped 'related experience in one or more of the following' requirement under Qualifications — treated as hard gates given their placement in the required qualifications section, though the 'one or more' framing means not all three are individually mandatory.
Machine learning and reinforcement learning appear only under Preferred Qualifications.
No total years of experience are specified at the role level.
PhD or internship experience in robotics planning is listed under Preferred Qualifications, as is knowledge of vehicle dynamics — these are domain knowledge areas rather than named tools and are omitted from the skills list per extraction rules.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): motion planning, trajectory optimization.
Read the full posting
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