Machine Learning Engineer

Responsibilities
1. Model Development: Implement and train supervised and unsupervised models using standard frameworks under structured guidance.
2. Model Evaluation: Evaluate models with appropriate metrics and perform error analysis to understand where and why a model fails.
3. Data Preprocessing & Feature Engineering: Clean, transform, and engineer features to prepare data for training and testing.
4. Experimentation: Apply basic statistical-analysis and experimental-design methods to tune and optimize models.
5. Deployment Support: Help package, deploy, and monitor models within ISL's infrastructure, learning CI/CD and containerization practices.
6. Collaboration: Work in a shared codebase using Git; partner with Data Research Engineers, AI Engineers, and Product Managers to deliver value.
7. Documentation: Clearly document training processes, experiment design, assumptions, and outcomes, and communicate results to the team.

Qualifications
1. Proficiency in English, both spoken and written, sufficient to collaborate, document, and present in an English-working environment.
2. Bachelor's degree in Computer Science, Statistics, Machine Learning, or a related quantitative field.
3. 2-4 years of experience in the related area depending on degree.
4. Hands-on experience building, training, and evaluating machine learning models with standard frameworks.
5. Highly independent and self-motivated, with the ability to manage multiple task items within a team environment.
6. Ability to learn from mistakes and a continuous drive to seek self-improvement in new skills and technical knowledge.
7. Strong collaborative mindset, acting as a good team player who understands how value is delivered both efficiently and responsively.
8. Proactive communicator who takes the initiative to voice concerns regarding technical risks and contributes to the creation and reduction of tech debt.