The organization is focused on verifying identities in real time and preventing identity fraud online using advanced machine learning and predictive analytics.
Requirements
- Proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Experience with deep learning models (especially CNNs) and familiarity with transformer models in computer vision.
- Strong understanding of supervised learning, model evaluation, and ML concepts like overfitting, regularization, and transfer learning.
- Experience with version control, experimentation tracking, and reproducible ML pipelines.
- Familiarity with cloud platforms (e.g., AWS, GCP) and containerization (e.g., Docker) is a plus.
Responsibilities
- Design and develop machine learning models for computer vision tasks such as image classification, object detection, or segmentation.
- Experiment with transformer-based architectures including ViT, DETR, and CLIP, and explore integration of LLMs in vision or multimodal applications.
- Collaborate with senior scientists and engineers on end-to-end model pipelines, from data preprocessing to deployment.
- Analyze large datasets and apply best practices in feature engineering, model tuning, and performance evaluation.
- Write production-quality, well-documented code and contribute to shared ML infrastructure and tools.
- Research and implement new ideas and methods through experimentation and innovation.
Other
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field with 2–5 years of experience, or Master’s degree with relevant academic or internship work.
- Strong communication skills and ability to work collaboratively in cross-functional teams.
- Equal opportunity and inclusive workplace.
- Accommodation available during hiring and onboarding process.
