Machine Learning Engineer - Scene Understanding

zoox

4100

Boston, MA, USA

Posted on: 2026-01-16

Category: emobility

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Employment type:

Full time

Experience required:

Intermediate

Salary

Salary not provided

About the company:

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.

The Perception Scene Understanding team at Zoox builds the high-performance reasoning engines that allow our autonomous vehicles to navigate complex urban environments. We translate sensor data and detected objects into deep semantic understanding, ensuring our robots make human-level decisions in real-time.

We are seeking engineers passionate about the intersection of robotics and cutting-edge AI. In this role, you will develop next-generation algorithms and deploy production-grade software directly to our fleet. You will tackle the inherent unpredictability of urban driving—including road closures, occlusions, dynamic traffic controls, and abnormal traffic patterns—ensuring our vehicles remain resilient in the face of unexpected hazards.

As an engineer on the Scene Understanding team, you will develop and deploy advanced machine learning models that interpret the robot’s surroundings to identify hazards and enforce driving restrictions. Your work will be pivotal in resolving unstructured scenarios, ensuring safe navigation through complex or restricted zones. You will optimize models for real-time inference on our vehicle hardware and collaborate closely with teams—including Localization, Perception, Prediction, and Planning—to deliver a seamless, safe, and reliable experience for our riders.

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. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to [email protected] 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.

In this role, you will...

Develop machine learning algorithms for Bird’s Eye View scene analysis and real-time hazard detection.

Design and implement scene understanding solutions specifically for Zoox's unique bidirectional vehicle.

Lead the end-to-end data strategy—including mining, auto-labeling, and dataset construction—to keep our ML models improving.

Implement on-vehicle software for model inference and result post-processing that runs reliably in real-time.

Use our large-scale data pipelines and infra to research, prototype, and deploy changes that directly improve how the vehicle handles complex road scenarios.

Team up with Perception, Prediction, and Planning. teams to integrate your signals into the final driving solution.

Qualifications

BS, MS, or PhD in Computer Science, Robotics, or a related field with 2+ years of industry experience training and deploying ML models.

Experience with BEV (Bird’s Eye View) models, segmentation, or 3D representations.

Hands-on experience with the full lifecycle: dataset construction, training frameworks, and building metrics pipelines to evaluate performance.

A strong mathematical foundation in linear algebra, calculus, and probability.

Fluency in C++ and Python. Experience with PyTorch, TensorFlow, JAX.

Bonus Qualifications

Track record of designing novel neural network architectures or working with probabilistic outputs.

Experience working on autonomous systems or integrating ML models into a robotics stack.

Publications in top-tier conferences like CVPR, ICCV, ECCV, RSS, or ICRA.

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