Zoox
Autonomous Vehicles
MachineLearningEngineer-SemanticReasoning(Highway)
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning Engineer - Semantic Reasoning (Highway) at Zoox. Skills: Semantic Reasoning, Deep Learning, Autonomous Vehicles, Machine Learning. Design, train, and deploy deep learning models. Adapt and elevate unified machine learning stack”
What You'll Achieve.
Achieve extended spatial range and high fidelity for highway environments; Ensure model outputs meet strict safety and clearance metrics; Ensure low-latency execution; Ensure vehicles remain safe and resilient
Industry & Context.
Tackle unpredictability of urban driving; Resolve perception-related regressions and edge cases
Work within rigorous compute constraints of the Zoox vehicle platform
What They're Looking For.
Must Have
MS (3–5 years) or PhD (0–2 years) in Computer Science, Robotics, Electrical Engineering, or a related field, professional software engineering experience, Deep understanding of 2D/3D computer vision, semantic segmentation, deep learning architectures, Exceptional programming skills in modern C++, Python, Hands-on experience with modern deep learning frameworks like JAX or PyTorch, Proven track record of deploying real-time machine learning models on resource-constrained embedded systems or on-bot hardware
Nice to Have
Prior experience dealing with highway autonomous driving scenarios and their specific mapping/perception challenges, Familiarity with state-of-the-art, BEV, Sparse Transformer architectures, Vision-Language Models (VLMs), publication record in top AI conferences or journals (e. g. , CVPR, ICCV, ECCV, ICML, NeurIPS)
What You'll Do.
and deploy deep learning models
Adapt and elevate unified machine learning stack
Define semantic representation requirements
Establish robust validation workflows
Optimize deep learning models for real-time inference
Investigate and resolve perception-related regressions
Contribute to long-term architecture
How You'll Work.
Team & Collaboration
Collaborate with partner Perception and motion planning teams; Collaborate with Scene Intelligence, Semantic Grounding, and PCP Mapping teams; Partner with downstream motion planning teams
Full Job Description
## Description The Scene Understanding Semantic Reasoning team at Zoox builds the high-performance reasoning engines that allow our autonomous vehicles to navigate complex driving environments and high-speed roads. We translate sensor data and detected objects into deep semantic understanding, ensuring our robots make human-level decisions in real-time. We are seeking experienced engineers passionate about the intersection of robotics and cutting-edge AI. In this role, you will focus on critical initiatives alongside partner Perception and motion planning teams to develop production-grade multi-task transformers, and integrate cutting-edge Vision Language Action (VLA) model outputs to build comprehensive spatial representations for our fleet. You will tackle the inherent unpredictability of urban driving on highways & freeways to improve range and accuracy, ensuring our vehicles remain safe and resilient at all times. ## In this role, you will... Model Training & Deployment: Design, train, and deploy deep learning models for semantic reasoning, specifically tailored to achieve the extended spatial range and high fidelity required for high-speed highway environments. Cross-Functional Collaboration: Collaborate with the Scene Intelligence, Semantic Grounding, and PCP Mapping teams to adapt and elevate the unified machine learning stack for highway scenarios. Requirements & Validation: Partner with downstream motion planning teams to define semantic representation requirements, establish robust validation workflows, and ensure model outputs meet strict safety and clearance metrics. Optimization: Optimize deep learning models for real-time inference efficiency, ensuring low-latency execution within the rigorous compute constraints of the Zoox vehicle platform. Edge Case Resolution: Investigate and resolve perception-related regressions and edge cases found in high-speed driving simulations and live fleet data. Strategic Architecture: Contribute to the long-term "North S
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