Choose where you would work from. We’ll compare it with the posting’s stated requirements.
Applying from U.S. Outlying Islands
Not enough information in this posting — confirm with the employer
Checked for applicants in U.S. Outlying Islands; employer sponsorship is not needed. Based on the source posting · not a hiring or work-authorization guarantee.
The posting does not specify whether applicants from your country are accepted.
Why this result?
3 still unclear
?The posting does not specify whether applicants from your country are accepted.
?Office attendance is required. Relocation and work permission have not been assessed.
This checks the posting’s stated requirements, not your legal right to work. Confirm details with the employer.
Requirements
1 with evidence8 unclear or unstated
Remote policy
hybrid
“Provider workplace type: hybrid”
From employer posting metadata
Employment type
Full-time
Compensation
USD 189,000–270,000 per-year-salary
8 fields not stated in this posting
Employer sponsorship
Not specified
Geographic scope
Not specified
Degree requirement
Not specified
Security clearance
Not mentioned
Timezone / hours
Not specified
Citizenship requirement
Not mentioned
Minimum experience (years)
Not specified
Work authorization
Not specified
These details were not stated in the tracked posting. Confirm with the employer.
READ IT IN THE EMPLOYER’S OWN WORDS
Original job description
Reading view uses normalized posting text so exact evidence excerpts can be highlighted.
The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance.
As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior.
In this role, you will:
Read full descriptionShow less description
You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc.
You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
You will develop metrics and tools to analyze errors and understand improvements of our systems
You will collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments
Qualifications
BS, MS, or PhD degree in computer science or related field
Experience with training and deploying transformer-based model architectures and reinforcement learning
Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
Fluency in C++ or Fluency in Python with a basic understanding of C++
Extensive experience with programming and algorithm design
Strong mathematics skills
Bonus Qualifications
Conference or Journal publications in Machine Learning or Robotics related venues
Prior experience with Prediction and/or autonomous vehicles or robotics in general
About ZooxZoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented 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.
Original formatting and links
The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance.
As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior.
In this role, you will:
You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc.
You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
You will develop metrics and tools to analyze errors and understand improvements of our systems
You will collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments
Qualifications
BS, MS, or PhD degree in computer science or related field
Experience with training and deploying transformer-based model architectures and reinforcement learning
Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
Fluency in C++ or Fluency in Python with a basic understanding of C++
Extensive experience with programming and algorithm design
Strong mathematics skills
Bonus Qualifications
Conference or Journal publications in Machine Learning or Robotics related venues
Prior experience with Prediction and/or autonomous vehicles or robotics in general
About ZooxZoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented 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.