Software Engineer, Machine Learning Platform

Key Skills

Aartificial intelligence, Algorithms, Apache Flink, Apache Hadoop, Apache Hive, Argo, AWS, Big Data Technology, Data structuring, Github, Google Cloud Platform (GCP), Java Programming, Kubeflow, Kubernetes-K8s, Linux Operating system, Machine learning techniques, MLflow, Presto, Python Programming, REST API, RESTful, Scala Programming, Unix Operating system

Job Description

About the role

Appier's mission is to make AI easier for everyone to use. To achieve this, we build AI systems to connect heterogeneous data in the cross-screen era, to process information efficiently while achieving our goals, and to solve new and exciting real-world problems. Are you passionate about AI, and looking to be part of this flourishing AI age? Do you want to witness how different types of data play a key role in concurrent AI systems and contribute to improving the capability of these systems? Do you want to build incredible AI systems and watch them grow to meet our product needs? If yes, we'd love to talk to you.

Responsibilities

• Build a flexible ML platform to speed up the development process of AI models.
• Develop next-generation AI backend systems related to large-scale data processing, real-time feature generation, evaluation/performance dashboard.
• Establish and maintain MLops processes and tools, including model deployment, monitoring, and automation.

About you

[Minimum qualifications]

• BS/BA degree in Computer Science or related field.
• Know basic software testing (able to write unit test for algorithms)
• Experience in Unix/Linux environments.
• Strong problem-solving skills and passion for learning new technologies.
• Great communication skills to work side-by-side with scientists, and collaborate with engineers, product managers and other teams.

[Preferred qualifications]

• Familiarity with the machine learning flow of building a data-driven AI system.
• Experience to use and extend ML platform like Kubeflow, MLflow, Apache Submarine
• Familiarity with cloud native ecosystems like k8s, helm, prometheus and argo.
• Experience with distributed computing engine (Spark) or streaming computing framework (Flink).
• Experience with Public Cloud like GCP.
• Experience in one of the following programming languages: Python/Java.