Job description for Machine Learning Engineer Intern at Lebry
Position: Machine Learning Engineer Intern
Type : Internship
Duration: minimum 4 months
Placement: IZI Charge, sister company of Lebry (Setiabudi, South Jakarta)
Join our IT team and get hands-on experience managing real infrastructure, from virtualized servers and networks to endpoint security and staff support. You will work closely with our systems engineer and software engineering team.
What You Will Do:
- Configure, containerize, and deploy local AI models and large language models (LLMs) on high-performance bare-metal workstations
- Help design and maintain data pipelines that collect telemetry and sensor data from hydroponic lab rooms and EV charging stations into centralized databases
- Build predictive algorithms to forecast operational metrics, such as EV charging peak-load times and optimal nutrient dosing cycles for crops like nasturtiums and micro-greens
- Work with our software engineering team to build and integrate machine learning APIs into internal dashboards and customer-facing applications
- Coordinate with our systems engineers to allocate training and inference workloads across Ubuntu servers and Proxmox VE environments
- Run experiments to benchmark model accuracy, latency, and resource efficiency, and fine-tune models for our internal business needs
- Clean, structure, and label datasets, turning raw IoT and operational data into model-ready formats
- Monitor GPU/CPU utilization, thermal metrics, and bottlenecks during training to keep hardware running efficiently
- Document model architectures, training sets, data lineage, and deployment SOPs
- Research and prototype AI tools that automate internal workflows or generate data insights for operations and marketing teams, including marketing analytics
Requirements:
- Final-year students or fresh graduates in Computer Science, Data Science, Statistics, Information Technology, or a related field
- Able to communicate in English, both written and spoken
- Basic understanding of Python and core libraries (NumPy, pandas, scikit-learn)
- Understanding of machine learning fundamentals and model evaluation
- Familiarity with Docker, Linux, and SQL is a plus
- Experience with PyTorch or TensorFlow, LLMs, or time-series forecasting is an advantage
- Relevant coursework, thesis, or portfolio projects (GitHub, Kaggle) are a plus
- Analytical mindset, curiosity, and strong problem-solving skills
