Simulation is essential for Zoox to rapidly iterate on our driving software and hardware and to validate our safety before we drive in the real world. We create virtual worlds to challenge our robots from real-world data entirely novel scenarios or a combination of both. Our simulations need to run at a huge scale to cover everything that might happen and to help prove our driving to be safe.
As a Machine Learning Engineer on the Simulation Core Team you will focus on the intersection of machine learning and synthetic environments within our high-speed GPU-based simulation framework. Our success depends on you driving ML efficiency while solving complex sim-to-sim and sim-to-real fidelity gaps ensuring our safety-critical models train on data that perfectly aligns with physical vehicle behavior.
In this role you will:
Develop and optimize our GPU-based simulation framework to support complex machine learning training and validation pipelines.
Apply reinforcement learning concepts to solve complex behavioral and path planning challenges in simulation environments.
Identify and resolve sim-to-sim and sim-to-real fidelity gaps to ensure parity between high-speed ML simulations high-fidelity 3D environments and physical vehicle execution.
Build systems that allow autonomy users to self-serve data generation and accelerate their training iterations.
Write robust production-ready code to integrate advanced ML algorithms directly into our core simulation architecture.
Qualifications:
PhD or Masters in computer science robotics machine learning or a related field.
Deep understanding of reinforcement learning and its application in simulated or robotic environments.
Hands-on experience developing training and fine-tuning deep learning models using modern frameworks (e.g. JAX or PyTorch).
Strong proficiency in C and Python for building and deploying production machine learning systems.
Experience analyzing and bridging fidelity gaps between synthetic training data and real-world execution.
Bonus Qualifications:
Experience with GPU programming (CUDA) or high-performance compute clusters.
Automotive or autonomous robotics industry experience.
Strong background in deterministic systems and latency optimization.
$151000 - $257000 a year
Base Salary Range
There are three major components to compensation for this position: salary Amazon Restricted Stock Units (RSUs) and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling as well as positioning within a level is determined by a range of factors including but not limited to a candidates relevant years of experience domain knowledge and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave vacation bereavement) unpaid time off Zoox Stock Appreciation Rights Amazon RSUs health insurance long-term care insurance long-term and short-term disability insurance and life insurance.
About Zoox
Zoox 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. Were looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
IC
Simulation is essential for Zoox to rapidly iterate on our driving software and hardware and to validate our safety before we drive in the real world. We create virtual worlds to challenge our robots from real-world data entirely novel scenarios or a combination of both. Our simulations need to run ...
Simulation is essential for Zoox to rapidly iterate on our driving software and hardware and to validate our safety before we drive in the real world. We create virtual worlds to challenge our robots from real-world data entirely novel scenarios or a combination of both. Our simulations need to run at a huge scale to cover everything that might happen and to help prove our driving to be safe.
As a Machine Learning Engineer on the Simulation Core Team you will focus on the intersection of machine learning and synthetic environments within our high-speed GPU-based simulation framework. Our success depends on you driving ML efficiency while solving complex sim-to-sim and sim-to-real fidelity gaps ensuring our safety-critical models train on data that perfectly aligns with physical vehicle behavior.
In this role you will:
Develop and optimize our GPU-based simulation framework to support complex machine learning training and validation pipelines.
Apply reinforcement learning concepts to solve complex behavioral and path planning challenges in simulation environments.
Identify and resolve sim-to-sim and sim-to-real fidelity gaps to ensure parity between high-speed ML simulations high-fidelity 3D environments and physical vehicle execution.
Build systems that allow autonomy users to self-serve data generation and accelerate their training iterations.
Write robust production-ready code to integrate advanced ML algorithms directly into our core simulation architecture.
Qualifications:
PhD or Masters in computer science robotics machine learning or a related field.
Deep understanding of reinforcement learning and its application in simulated or robotic environments.
Hands-on experience developing training and fine-tuning deep learning models using modern frameworks (e.g. JAX or PyTorch).
Strong proficiency in C and Python for building and deploying production machine learning systems.
Experience analyzing and bridging fidelity gaps between synthetic training data and real-world execution.
Bonus Qualifications:
Experience with GPU programming (CUDA) or high-performance compute clusters.
Automotive or autonomous robotics industry experience.
Strong background in deterministic systems and latency optimization.
$151000 - $257000 a year
Base Salary Range
There are three major components to compensation for this position: salary Amazon Restricted Stock Units (RSUs) and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling as well as positioning within a level is determined by a range of factors including but not limited to a candidates relevant years of experience domain knowledge and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave vacation bereavement) unpaid time off Zoox Stock Appreciation Rights Amazon RSUs health insurance long-term care insurance long-term and short-term disability insurance and life insurance.
About Zoox
Zoox 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. Were looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
We’re reinventing personal transportation—making the future safer, cleaner, and more enjoyable for everyone. This is on-demand autonomous ride-hailing.
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