About Appier
Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180).
Visit www.appier.com for more information.
About the role
As a Machine Learning Engineer Intern, you will be an integral part of our engineering team, contributing to the development and enhancement of advanced machine learning models and systems. You will have the opportunity to work on various projects aimed at improving machine learning workflows, including data processing, model training, deployment, and monitoring. This internship offers a unique chance to gain hands-on experience in the field of artificial intelligence and machine learning, contributing to impactful projects within a dynamic team.
We are currently looking for individuals who can commit to an internship schedule of 2-5 days (16-40 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.
Responsibilities
- Collaboration:
- Collaborate with senior engineers and data scientists to design and implement experiments to improve machine learning models' performance.
- Participate in team meetings, contributing ideas to drive innovation in machine learning projects.
- Model Deployment: Assist in deploying machine learning models into production environments using CI/CD pipelines.
- Monitoring & Logging: Set up monitoring and logging for ML models to ensure reliability, and build dashboards to ensure performance in production.
- Data Management: Work on data pipeline automation, and ensuring data quality throughout the ML pipeline.
- Documentation: Maintain clear and concise documentation of all processes, tools, and models deployed.
- Automation: Contribute to the automation of repetitive tasks related to model training, testing, and deployment.
- Infrastructure Management: Help manage and optimize ML infrastructure on cloud platforms (e.g., AWS, GCP, Azure) for scalability and performance.
About you
[Minimum qualifications]
- Currently enrolled in an undergraduate or graduate program (Bachelor's, Master's, or Ph.D.) in computer science, artificial intelligence, machine learning, or a related field.
- Strong programming skills in languages such as Python/Golang.
- Basic understanding of large language model (LLM) concepts and experience with LLM providers like OpenAI, Anthropic, and Mistral AI.
- Basic understanding of DevOps practices and tools such as Docker and Kubernetes.
- Excellent problem-solving skills and analytical thinking.
- Ability to work independently as well as collaboratively in a team environment.
- Good communication skills with the ability to present complex ideas effectively.
[Preferred qualifications]
- Previous experience or coursework in large language model (LLM) or machine learning.
- Familiarity with deep learning frameworks and libraries.
- Experience with large-scale data processing and distributed computing platforms.
- Demonstrated interest in advancing machine learning techniques and applications.
- Experience in building and deploying automation and continuous integration systems.
- Experience in operating services on IaaS such as AWS and GCP.
