Machine Learning Engineer – Semantic Reasoning

Zoox

Machine Learning Engineer developing high-performance reasoning engines allowing autonomous vehicles to navigate complex driving environments. Collaborating with multiple teams to ensure safety and efficacy of models.

Posted 5/30/2026full-timeFoster City • California, Massachusetts • 🇺🇸 United StatesMid-LevelSenior💰 $189,000 - $258,000 per yearWebsite

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

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.
Collaborate with the Scene Intelligence, Semantic Grounding, and PCP Mapping teams to adapt and elevate the unified machine learning stack for highway scenarios.
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.
Optimize deep learning models for real-time inference efficiency, ensuring low-latency execution within the rigorous compute constraints of the Zoox vehicle platform.
Investigate and resolve perception-related regressions and edge cases found in high-speed driving simulations and live fleet data.
Contribute to the long-term "North Star" architecture for Perception Semantic Reasoning, paving the way for scalable fleet deployment across new vehicle platforms.

Requirements

What you’ll need

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).

Benefits

Comp & perks

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
life insurance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

deep learningsemantic reasoning2D computer vision3D computer visionsemantic segmentationC++PythonJAXPyTorchreal-time machine learning

Soft Skills

collaborationproblem-solvingcommunication