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# Machine Learning Engineer - Semantic Reasoning (Highway)

- **Company:** Zoox
- **Location:** Boston, MA, United States
- **Experience:** Experienced
- **Salary:** $189,000.0 - $258,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Computer Programming, Python (Programming Language), Machine Learning, Language Modeling, Motion Planning, Software Engineering, Pytorch, Deep Learning, Information Technology, C++14
- **Published:** May 31, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7cc30c48051717c5

## About the Role

Do you have experience in Software engineering?, 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., * MS (3-5 years) or PhD (0-2 years) in Computer Science, Robotics, Electrical Engineering, or a related field, with professional software engineering experience - ideally in autonomous driving, robotics, or computer vision.

 * Deep understanding of 2D/3D computer vision, semantic segmentation, and deep learning architectures.
 * Exceptional programming skills in modern C++ and 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., * Prior experience dealing with highway autonomous driving scenarios and their specific mapping/perception challenges.
 * Familiarity with state-of-the-art, BEV, Sparse Transformer architectures and Vision-Language Models (VLMs).
 * Strong publication record in top AI conferences or journals (e.g., CVPR, ICCV, ECCV, ICML, NeurIPS).

## Description

* 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 Star" architecture for Perception Semantic Reasoning, paving the way for scalable fleet deployment across new vehicle platforms.


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