Machine Learning Engineer

Machine Learning Engineer

About the Role

We are looking for a passionate Machine Learning Engineer to join our growing AI team. In this role, you will contribute to the development of intelligent systems with a primary focus on Reinforcement Learning, while also gaining hands-on experience with Large Language Models, Generative AI, and Computer Vision technologies.

You will collaborate closely with experienced engineers and researchers, participate in real-world AI projects, and continuously develop your technical capabilities through challenging engineering problems and on-the-job learning.

What You'll Work On

   • Develop and improve intelligent algorithms and learning-based systems,

   • Contribute to Reinforcement Learning projects involving training, evaluation, and optimization of autonomous agents,

   • Apply Transformer architectures, Large Language Models (LLMs), and Generative AI approaches to practical engineering problems,

   • Participate in machine learning workflows including data preparation, model training, evaluation, debugging, and performance analysis,

   • Support Computer Vision applications such as image classification, object detection, feature extraction, and image understanding,

   • Build scalable, modular, and maintainable software components for AI-driven applications,

   • Analyze research papers and open-source implementations to adapt state-of-the-art methods to real-world use cases,

   • Contribute to simulation-based and data-driven AI solutions,

   • Collaborate with multidisciplinary teams and continuously improve technical capabilities through hands-on experience.

Qualifications

   • Bachelor's or Master's degree in Computer Science, Software Engineering, Electrical Engineering, Artificial Intelligence Engineering, or a related discipline,

   • Strong Python programming skills and solid understanding of Object-Oriented Programming principles,

   • Good knowledge of algorithms, data structures, and software engineering fundamentals,

   • Ability to write clean, maintainable, and reusable code,

   • Familiarity with debugging, documentation, testing, and version control practices,

   • Hands-on experience or strong motivation to work with PyTorch and modern deep learning frameworks,

   • Basic understanding of machine learning workflows including data preparation, model training, evaluation, debugging, and model improvement,

   • Understanding of Reinforcement Learning fundamentals, including agent, environment, state, action, and reward concepts,

   • Familiarity with training, evaluation, and optimization of learning-based agents, with a strong interest in applying Reinforcement Learning to real-world problems,

   • Ability and motivation to understand and implement methods from research papers or open-source projects,

   • Interest in Transformer architectures, attention mechanisms, Large Language Models, and Generative AI technologies,

   • Basic knowledge of Computer Vision tasks such as image classification, object detection, feature extraction, or image understanding,

   • Experience with Git and collaborative software development environments,

   • Familiarity with Linux-based development environments and modern software architectures,

   • Exposure to simulation environments, agent-based systems, or parallel processing techniques is considered a plus,

   • Strong analytical thinking, problem-solving skills, ownership mindset, and eagerness to learn emerging AI technologies,

   • Good written and verbal communication skills in English.

Preferred Qualifications

   • Experience with PyTorch-based model development and training,

   • Familiarity with Hugging Face ecosystem, model fine-tuning, or inference optimization techniques,

   • Knowledge of ONNX, TensorRT, quantization, or model deployment approaches,

   • Experience with multiprocessing, parallel computing, or efficient data processing pipelines,

   • Exposure to simulation environments or agent-based systems,

   • Academic, personal, or project experience in Reinforcement Learning is considered a plus,

   • Familiarity with Deep Q-Network (DQN), Policy Gradient, Actor-Critic, PPO, or other Reinforcement Learning methods is advantageous,

   • Interest in implementing and adapting Reinforcement Learning algorithms from research papers or open-source projects

   • Familiarity with Transformer architecture and attention mechanisms,

   • Basic understanding of tokenization, embeddings, fine-tuning, and inference,

   • Ongoing M.Sc. studies or research experience in Reinforcement Learning, Generative AI, Large Language Models, or Computer Vision,

   • Contributions to open-source projects, academic publications, or personal AI projects are considered a plus.

Position Details

• Department: Explainable AI and Augmented Intelligence (AI)
• Position name: Software Engineer
• Employment Type: Full-time
• Location: İzmir