Key Facts

Remote From:

Full time

English

Hard Skills

Transformer Design Computer Vision Deep Learning Data Localization C++ (Programming Language) Python (Programming Language) Annotation Processing Analytics Systems Integration Digital Cameras TensorFlow Digital Mapping Data Curation PyTorch (Machine Learning Library) Academic Writing Autonomous Vehicles Very Large Databases (VLDB) Sensor Fusion Data Architecture

Other Skills

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Spatial Abilities
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Plan Execution
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Teamwork
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Adaptability
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Problem Solving

Roles & Responsibilities

Advanced degree (Ph.D. or Master's) in computer science, computer engineering, robotics, mathematics, or related fields
In-depth knowledge and extensive experience in deep learning, computer vision, and modern transformer architectures
Hands-on experience with ML frameworks such as PyTorch or TensorFlow
Solid programming skills in Python and preferably C++

Requirements:

Design, train, and deploy deep learning models for lane marking and road feature detection using camera, LiDAR, and other sensor data
Develop transformer-based architectures and leverage other modern deep learning techniques for spatial-temporal perception and HD map updating
Handle complex scenarios such as poorly painted lanes and temporary construction areas in dynamic weather conditions
Collaborate with perception, localization, and planning teams to integrate learning-based map components into the autonomous driving system

Job description

Why Join Us

At Bot Auto, you’ll work on cutting-edge autonomous technologies that directly impact how self-driving cars perceive and navigate the world. You’ll collaborate with experts across AI, mapping, and robotics, shaping the next generation of intelligent mapping systems.

About the Role

We are seeking a highly motivated Machine Learning Engineer to join our HD mapping team. The ideal candidate will develop learning-based algorithms for online map building in both well-maintained road environments and challenging construction zones. You will leverage cutting-edge deep learning and transformer-based architectures to improve our real-time mapping and perception systems, which serve as the foundation for safe and scalable autonomous driving.

Key Responsibilities

Design, train, and deploy deep learning models for lane marking and road feature detection using camera, LiDAR, and other sensor data.
Develop transformer-based architectures and leverage other modern deep learning techniques for spatial-temporal perception and HD map updating.
Handle complex scenarios such as poorly painted lanes and temporary construction areas in dynamic weather conditions.
Collaborate with perception, localization, and planning teams to integrate learning-based map components into the autonomous driving system.
Conduct data analysis, dataset curation, and annotation for model training and evaluation.

Qualifications

Required

Have an advanced degree (Ph.D or Master’s) in related fields of study: computer science, computer engineering, robotics, mathematics, and etc.
In-depth knowledge and extensive experience in deep learning, computer vision, and modern transformer architectures.
Hands-on experience with ML frameworks such as PyTorch or TensorFlow.
Solid programming skills in Python and preferably C++.
Strong problem-solving skills and ability to work in a fast-paced, research-driven environment.

Preferred

Have a proven track record of research publications in top machine learning conferences and/or journals.
Prior experience in autonomous driving perception, semantic segmentation, online map generation, or multi-modal sensor fusion is highly desirable.
Experience with real-world deployment of perception models in robotics or autonomous systems.
Background in handling large-scale datasets and real-time processing pipelines.