Machine Learning Engineer - Intelligent Safety

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Didi autonomous driving is innovating the way we build our self-driving cars. We leverage our ride-sharing network to collect large amounts of data, and improve our algorithms with the data. In this role, you will design, train and deploy cutting-edge ML models to solve safety critical issues on the road . This is a multidisciplinary role that will get you exposed to system engineering, perception, prediction, motion planning and controls, and embedded computing. It covers different algorithms across the self-driving car stack. If you are interested in solving exciting and challenging problems, and are enthusiastic about autonomous driving, please join us!

Responsibilities:

• Exploring, prototyping, validating, and iterating new machine learning algorithms to enable safer AV behavior and scale to multiple cities.
• On the development side, productionize ML algorithms to improve onboard intelligence of autonomous vehicles, including training, serving, evaluating and monitoring the ML models
• Champion of the best engineering practices, crafting solutions and not just identifying problems

Qualifications:

• BS, MS, or higher degree, in CS/CE/EE, or equivalent industry experience
• Extensive experience with ML frameworks such as Tensorflow or PyTorch
• Strong experience with state-of-the-art machine learning.
• Strong programming skills in Python and/or C++.
• Experience with the ML model lifecycle management (labeling, training, productization, and maintenance)
• Passionate about self-driving car technology and its impact on the world!

Preferred Qualifications:

• Masters or Ph.D. degree in Computer Science, Electrical and Computer Engineering or a related field with a focus in computer vision, robotics, machine learning, artificial intelligence.
• Solid research papers on AI/ML or robotics conferences / journals
• Experience and deep understanding with any of the following domains is a plus:
• Experience with CUDA
• Experience with developing custom ops for performant ML inference models
• Experience with ROS, OpenCV, or PCL.
• Deep learning related projects such as ML planning, prediction, object recognition / segmentation using multisensor etc.