Hands-On AI Model Trainer — Data, Evaluation & Innovation

CHRONOAI PTE. LTD.; Singapore; Presencial; SGD 60.000 - 90.000; Jornada completa

Descripción de la vacante

ChronoAI is seeking an AI Model Trainer in Singapore to advance model capabilities through better data, training methods and evaluation. You will turn complex knowledge into structured training data and design experiments from dataset design to evaluation.

You will collaborate with AI/ML engineers and leadership, own hypotheses, and drive hands-on training, fine-tuning and evaluation of language and reasoning models.

Formación

• Degree in CS/AI/DS/Math/Stats/Engineering/Physics or related.
• Hands-on ML experience with training, fine-tuning, data preparation or evaluation.
• Proficient in Python and ML frameworks.
• Ability to design experiments and reason about failures.
• Experience with language/model training is a plus.

Responsabilidades

• Develop, curate and validate high-quality datasets for model training.
• Translate domain knowledge into machine-trainable examples.
• Run training, fine-tuning and iterative optimisation experiments.
• Design evaluation criteria and benchmarks.
• Analyse model outputs for accuracy and reasoning quality.
• Identify failure patterns and hypothesise causes.
• Experiment with prompting, synthetic data and model selection.
• Ensure data quality and avoid error amplification at scale.
• Document datasets, methods and results for reproducibility.
• Use AI tools to accelerate repetitive work with human oversight.

Conocimientos

Machine learning

Python

Data preparation

Evaluation

Analytical reasoning

Experiment design

Reproducibility

PyTorch

Hugging Face

LoRA/QLoRA

Educación

Bachelor's degree in CS/AI/DS/Math/Stats/Engineering/Physics

Statistics/Math background

Herramientas

PyTorch

Hugging Face Transformers

LoRA/QLoRA

PEFT

Descripción del empleo

ChronoAI is seeking an AI Model Trainer in Singapore to advance model capabilities through better data, training methods and evaluation. You will turn complex knowledge into structured training data and design experiments from dataset design to evaluation.

You will collaborate with AI/ML engineers and leadership, own hypotheses, and drive hands-on training, fine-tuning and evaluation of language and reasoning models.