Role Overview
We are seeking a Machine Learning Engineer to help design and implement intelligent systems that enhance user experiences across a large-scale digital platform. This role focuses on applying data science and machine learning techniques to solve real-world problems in areas such as personalization, content optimization, and user engagement.
You will collaborate with both technical and business teams to translate complex challenges into scalable ML-driven solutions, with a strong emphasis on experimentation and measurable impact.
What You'll Work On
- Build machine learning models that power personalization features such as recommendations and targeted content delivery
- Develop solutions for areas including natural language processing, image-based optimization, and user behavior modeling
- Partner with stakeholders to define problems, success metrics, and data-driven strategies
- Design and run experiments to validate model performance and business impact
- Deploy models into production environments through APIs and scalable systems
- Continuously iterate on solutions using insights derived from data and experimentation
Key Responsibilities
- Translate business problems into machine learning frameworks and technical designs
- Implement and optimize models using modern ML and deep learning techniques
- Conduct controlled experiments (e.g., A/B testing, multi-armed bandits) to evaluate different approaches
- Analyze experimental results using statistical methods to assess effectiveness
- Work cross-functionally with data engineers, backend engineers, and product teams
- Contribute to improving data pipelines, feature engineering, and model deployment processes
Core Requirements
- Strong interest and curiosity in machine learning and data science
- Advanced degree (Master's or higher) in a quantitative field such as computer science, mathematics, or related discipline—or equivalent practical experience
- Solid understanding of machine learning fundamentals, including model types, optimization techniques, and evaluation methods
- Hands-on experience building ML or deep learning models (e.g., NLP, computer vision, or recommendation systems)
- Strong programming skills in Python
- Experience using deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with GPU-based model training workflows
- Experience designing and analyzing experiments, including hypothesis testing
Preferred Expertise
Machine Learning & Theory
- Knowledge in advanced topics such as:
- Counterfactual or off-policy learning
- Transformer architectures and attention mechanisms
- Optimization and probabilistic modeling techniques
Mathematics & Statistics
- Strong foundation in:
- Linear algebra and calculus
- Probability theory and statistical inference
- Exposure to advanced mathematical concepts (e.g., stochastic processes, geometric methods) is a plus
Experimentation & Analytics
- Experience with:
- Classical A/B testing frameworks
- Bayesian experimentation methods
- Optimization techniques such as bandit algorithms
Nice to Have
- Experience deploying machine learning models into production systems
- Track record of research contributions or publications in recognized conferences or journals
- Experience working in multilingual or international environments
- Additional language skills are a plus
