Machine Learning Engineer (Intern)
- Taipei City
- Training
- Full-time
Visit for more information.About the roleWe are looking for a Machine Learning Engineer Intern to join the Enterprise Solution Science Team.
This team focuses on applying cutting-edge ML technologies to real-world marketing problems by combining them with omnichannel customer data.
In this role, you will help bridge the gap between research and production by building and optimizing scalable, high-performance ML infrastructure - including data pipelines, dashboards, and monitoring systems.We are currently looking for individuals who can commit to an internship schedule of 2~4 days (16~32 hours) per week. This internship opportunity entails a minimum duration of 6 months, beginning from the present date. We advise prospective applicants to carefully assess their availability for this commitment before submitting their applications.[ Due to the hybrid work model, this position cannot be fully remote and requires working in the Taiwan office. ]What You'll Work On
- Assist in operating robust ML job execution frameworks for training, inference, and post-processing.
- Assist in building and maintaining internal API servers and developer tools to orchestrate ML jobs on Kubernetes (via Argo Workflows, Helm, Terraform).
- Assist in implementing data infrastructure and monitoring tools like Prometheus and Grafana.
- Create internal tools and services to simplify ML experimentation and production workflows.
- Collaborate closely with senior ML engineers and scientists to turn research outputs into user-facing product features
- Bachelor's degree in Computer Science, Engineering, or a related field (Master's degree preferred)
- 2+ years of experience in ML or engineering
- Proficiency in at least one programming language such as Python, Java, or Go, along with solid understanding of data structures and algorithms
- Impact-driven mindset, strong analytical and problem-solving skills, and a continuous passion for learning cutting-edge technologies.
- Familiar in using LLM-powered tools (e.g., Github Copilot, ChatGPT) to boost development productivity
- Understanding of core ML and deep learning concepts
- Hands-on experience with end-to-end ML workflows and AI system architecture, and familiarity with platforms like Kubeflow, MLflow, or Apache Submarine.
- Proficiency with cloud-native ecosystems (e.g., Kubernetes, Helm, Prometheus, Argo Workflows)