Software Engineer - Deep Learning &Data Infra -C

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

The Machine Learning infra team builds and supports the essential tools, frameworks and pipelines for every machine learning engineer at Didi autonomous driving. Our goal is to fully utilize the enormous quantity of the daily collected sensor and driver behavior data, greatly accelerate the development cycle of machine learning models across the whole company, empowering machine learning engineers to focus on improving the car’s safety and performance, instead of worrying about their infrastructure. Our scope covers the whole lifecycle of machine learning: intelligent data collection, data mining and data annotation, model training, model evaluation and debugging, model optimization and deployment to vehicles. We care about performance, ease of use and reliability of our products.

Responsibilities:

• Design, implement and deploy offboardinfra and tools to support machine learning models training/optimization/deployment and data collection/mining/annotation workflows.
• Own technical projects from start to finish and be responsible for major technical decisions and tradeoffs. Effectively participate in team’s planning, code reviews and design discussions.
• Consider the effects of projects across multiple teams and proactively manage conflicts. Work closely with partner teams to ensure they are benefiting from the systems we built.

Qualifications:

• Strong coding in Python and experience with C++.
• Must have the experience in at least one of the following:
• Data mining (e.g. active learning) for autonomous driving
• Automatic data annotation
• Passionate about self-driving technology and its potential impact on the world
• BS, MS or PhD in CS, Math or equivalent real-world experience

Preferred:

• Experience with deep learning frameworks like PyTorch, TensorFlow, etc.
• Experience with MLOps platforms such as Kubeflow etc.
• Understanding of distributed ML model training, model deployment (e.g., TensorRT conversion)
• Experience building software solutions on cloud infrastructure
• Experience working with Docker and Kubernetes
• Knowledge and experience with machine learning algorithms
• 3+ years of experience in the industry