Job Description

ESSENTIAL DUTIES AND RESPONSIBILITIES:

AI Algorithm Development Intern (Reinforcement Learning Focus)

We are looking for an AI Algorithm Development Intern to design and develop a self-learning AI agent capable of improving its performance through repeated interactions.

This role provides hands-on experience in Artificial Intelligence, Reinforcement Learning, and adaptive decision-making systems. You will focus on designing learning algorithms, optimizing strategy performance, and analyzing model behavior through iterative experimentation.

This position emphasizes AI algorithm development and learning system design, not hardware or robotics development.

Responsibilities:

Design and implement a Reinforcement Learning-based AI agent capable of learning and improving over time
Define and structure:
- State space
- Action space
- Reward functions
- Learning policies
Develop training pipelines and learning loops to enable performance improvement across repeated simulations
Analyze model performance, convergence behavior, and win-rate optimization
Experiment with different algorithms such as:
- Q-Learning
  SARSA
- Deep Q-Network (DQN)
- Policy-based methods
Optimize model parameters and improve learning efficiency
Conduct benchmarking and comparative evaluation of different AI strategies
Document algorithm design, experimental results, and learning insights
Present findings and performance improvements clearly to stakeholders

What You Will Gain:

Hands-on experience in Reinforcement Learning and Game AI
Practical exposure to AI experimentation and performance optimization
Experience implementing AI models from scratch
Opportunity to work on real-world adaptive decision systems

This position is part of our Early Career program at WD. Our Early Career program is designed to support individuals beginning their professional career by providing the foundational training through a structured onboarding, mentorship, and development curriculum.

Qualifications

REQUIRED:

Current student pursuing Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learningor equivalent experience.
Strong foundation in:
- Python programming
- Linear Algebra, Probability, and Statistics
- Algorithms and Data Structures
Basic knowledge of:
- Machine Learning concepts
- Model training and evaluation
- Optimization techniques
Strong analytical and problem-solving skills
Good command of English (verbal and written)
Internship period: Minimum 3 months starting July 2026

PREFERRED:

Knowledge or project experience in:
- Reinforcement Learning (Q-Learning, SARSA, Policy Gradient, Deep Q-Network)
- Game AI development
- Self-learning or adaptive systems
- Strategy optimization algorithms (Minimax, Monte Carlo Tree Search)
Experience with:
- TensorFlow or PyTorch
- Simulation environments
- Reward function design
- Performance benchmarking and model tuning
Experience implementing AI models from scratch (not only using high-level libraries)

SKILLS:

Ability to design and implement an AI algorithm that learns from repeated interactions and improves decision-making over time
Ability to:
- Define state space and action space
- Design reward mechanisms
- Implement training loops and convergence logic
- Evaluate model performance and learning efficiency
Strong debugging and experimentation mindset
Ability to analyze learning behavior and optimize strategy performance
Independent research ability and fast learning capability

Project Scope

Develop a self-learning AI agent capable of:

Playing Tic-Tac-Toe against a human opponent
Learning from each game outcome
Improving win-rate over time using reinforcement learning techniques
Optimizing strategy through iterative training and evaluation

Focus area: AI Algorithm Design, Learning Strategy, and Performance Optimization

Additional Information

#LI-BI1

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