- Workplace
- Hybrid
- Type
- Full-time
- Level
- Not stated
- Salary
- $199K – $270K/yr
The Behavior Capabilities opportunity falls within the Fail Operational Planning team at Zoox. This team works to ensure that the robot continues to operate effectively, even in the presence of minor compartmentalized failures or degraded capabilities. The focus of the team includes developing behavioral and motion planning algorithms to improve trip progress, vehicle uptime metrics, and correspondingly a better customer experience. This is a cutting-edge problem which requires teamwork and collaboration across a diverse group of experts including Planner, Perception, Prediction, Hardware, and more.
On this team, you will contribute to the next generation of algorithmic advancements to expand our robot’s ability to drive in some of the most complex scenarios. We are looking for an engineer who is excited to understand the interdependencies within the driving stack and takes ownership of their projects and has a holistic product focused approach towards development.
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AccommodationsIf you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.
A Final Note:You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
In this role, you will:
- Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base.
- Collaborate cross-functionally to develop strategies and intelligent behaviors to mitigate the performance degradation under various failure modes of the system.
- Architect and implement scalable solutions ensuring seamless integration with the rest of the driving stack.
- Partner with validation teams to define and execute tests for developed algorithms.
Qualifications
- Master's degree in Computer Science or related field
- Fluency in C++
- Extensive experience with programming and algorithm design
- Understanding of vehicle kinematics
- 4+ years of experience in a related field
Bonus Qualifications
- PhD in Computer Science, Electrical Engineering, Mechanical Engineering, or related field
- Experience shipping products to market (commercial robotics, drones, consumer devices etc.)
- Demonstrated ability to create real-time motion planning algorithms in a cross-functional team
Follow us on LinkedIn
AccommodationsIf you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.
A Final Note:You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
JobsPing insights
Our own analysis of this role, based on every open job we track. Updated daily.
Salary benchmark
Software Engineering roles in United States · 329 posted USD salaries
25th: $242kMedian $292k75th: $356k
This role's midpoint is about 20% below the median for comparable listings. How to negotiate
How in-demand these skills are
Share of all open roles on JobsPing that ask for each skill
- Machine Learning19.7% · 1,841
- C++5.9% · 551
Zoox right now
- Open roles
- 229
- Remote
- 0%
- New this week
- 3
Skills they ask for most: Machine Learning, Python, C++, LLM / GenAI.
How to stand out for this role
- Lead your resume bullets with shipped outcomes: latency cut, revenue moved, incidents reduced.
- Link one repository or project that uses this job’s main stack, and make sure its README explains what you built.
- Mirror the posting’s exact tool names (e.g. “PostgreSQL”, “Next.js”) for skills you genuinely have.
- Prepare one story about a production bug you debugged end-to-end; it comes up in almost every loop.