🕒 7 days ago
Build and productionize machine-learning models that analyze ad visuals, audio, and text across languages, improving brand safety, moderation, creative generation, and advertising outcomes at scale.
Machine Learning Engineer - Vision Foundation Models
🕒 5 days ago
Computer VisionDeep LearningImage And Video UnderstandingObject Detection
📜 Description
- Own a model or feature area end to end, including data curation, labeling guidelines, training, evaluation, and deployment.
- Use rigorous evaluation and error analysis to ensure models meet quality standards for live operations.
- Deploy models within real-time and compute constraints of the onboard perception system.
- Partner with product, operations, safety, and vehicle software teams to define model specifications, metrics, and validation plans.
🛠️ Requirements
- MS or PhD in Computer Science, Machine Learning, Computer Vision, or a related field, with 6+ years of industry experience; PhD candidates require 3+ years.
- Deep expertise in computer vision and deep learning for image and video understanding, including object detection, pose and action recognition, and fine-grained classification.
- Hands-on experience with convolutional and vision-transformer detectors and self-supervised or foundation-model backbones such as DINOv3.
- Strong proficiency in Python and PyTorch for model design, training, and evaluation.
- Experience with vision-language models or foundation-model backbones, and auto-labeling or synthetic data for long-tail and rare classes.
- Experience owning the ML lifecycle on real-world data, including dataset curation, labeling guidelines, metric design, and error analysis.
- Bonus: experience with in-cabin or occupant monitoring, multi-camera or fisheye imagery, or low-light conditions.
- Bonus: real-time model deployment with TensorRT, ONNX, or similar; C++ proficiency for model integration.
✨ Benefits
- Design, train, and evaluate in-cabin vision models for passenger, object, and behavior understanding, including detection, pose, and action recognition.
Full job description
The Perception team at Zoox is responsible for how our purpose-built robotaxi understands the world, including what happens inside the cabin. In this role, you will design, train, and evaluate vision-based in-cabin perception models that understand passengers, objects, and behaviors inside the vehicle. These models support rider safety and the operations that keep each ride running smoothly.
You will independently own a model area end to end, from data curation and training through evaluation and deployment under real-time and compute constraints, working closely with partners across the company. Your work has a direct impact on rider safety and experience as Zoox scales its robotaxi service.
In this role, you will...
Design, train, and evaluate vision models that understand passengers, objects, and behaviors inside the vehicle, across detection, pose, and action recognition.
Independently own a model or feature area end to end: curate data, define labeling guidelines, train and evaluate models, and take them through deployment.
Set the quality bar for the models you own, using rigorous evaluation and error analysis so they are reliable enough to support live operations.
Deploy models under real-time and compute constraints shared with the rest of the onboard perception system.
Partner across product, operations, safety, and vehicle software to turn requirements into model specifications, metrics, and validation plans.
Qualifications
MS or PhD in Computer Science, Machine Learning, Computer Vision, or a related field with 6+ years of industry experience (PhD with 3+ years).
Deep expertise in computer vision and deep learning for image and video understanding: object detection, pose and action recognition, and fine-grained classification, with hands-on experience with modern architectures (convolutional and vision-transformer detectors, and self-supervised or foundation-model backbones such as DINOv3).
Strong proficiency in Python and PyTorch for model design, training, and evaluation.
Experience with vision-language models or foundation-model backbones, and with auto-labeling or synthetic data for long-tail and rare classes.
Track record of owning the full ML lifecycle on real-world data: dataset curation and labeling guidelines, training, metric design (precision, recall, and false-positive rate at an operating point), and error analysis.
Bonus Qualifications
Experience with in-cabin or occupant monitoring (occupant or driver monitoring systems), ideally including multi-camera or fisheye imagery and low-light conditions.
Experience deploying deep learning models to real-time, resource-constrained systems (robotics, automotive, or edge devices) with TensorRT / ONNX or similar.
Proficiency in C++ for model integration.
Similar jobs
Search more Machine Learning Engineer jobs
Experienced Deep Learning Algorithm Developer - 3D Perception Team
🕒 8 days ago
Mobileye👥 1001 - 5000 employees🏢 Automotive
🕒 8 days ago
Develop and optimize transformer-based temporal vision models for 3D perception, using large-scale driving data and rigorous experimentation to improve autonomous-vehicle scene understanding.
Senior Software Engineer, Machine Learning Platform
🕒 4 days ago
Airwallex👥 10,000+ employees🏢 Financial Services🤝 B2B
🕒 4 days ago
Build and scale the machine learning platform behind risk decisioning, developing reliable model and data infrastructure that helps teams deploy, monitor, and improve models protecting transactions across the platform.
🕒 10 days ago
Lead the development and deployment of production-grade machine learning systems, shaping architecture and engineering standards while advising clients and mentoring engineers.
🕒 10 days ago
Lead the deployment and operational reliability of production LLM applications and agentic AI workflows, building secure, observable, and scalable services across Kubernetes, data pipelines, and cloud infrastructure.