AI Engineer: Generative AI & RL (On-Site Istanbul)

Good Job Games; Fatih; On-site; TRY 350,000 - 650,000; Full time

Benefits offered by this job

Relocation support

Great food

On-site gym

Compensation for paid military service

Team events and trips

Good Job Games Coin Program

Job summary

Good Job Games is seeking an AI enthusiast to join our Istanbul team for leading AI research and development across internal products and management solutions. You will work with RL agents, segmentation, regression models, and multi-modal systems to drive product innovation.

The role emphasizes building solutions from big data insights, experimenting with self-learning agents, and deploying state-of-the-art generative models.

Qualifications

• Bachelor's degree in Computer Science, Mathematics or a related engineering field.
• Proficiency in Python with TensorFlow or PyTorch.
• Strong interest in Generative AI techniques (Diffusers, LoRAs, GANs, Transformer models).

Responsibilities

• Lead AI research and development for internal products and management solutions (RL agents, segmentation, regression models).
• Analyze big data to extract features, segment, and provide actionable insights for management.
• Research, implement, and experiment with self-learning agents in diverse RL environments for product development.
• Follow and deploy cutting-edge generational models (including finetuning) for multi-modal use cases.
• Be at the forefront of ML/DL research and gain valuable expertise.

Skills

Python (TensorFlow or PyTorch)

Generative AI techniques (Diffusers,Lo

Education

Bachelor's degree in Computer Science, Mathematics or a related engineering field

Job description

Good Job Games is seeking an AI enthusiast to join our Istanbul team for leading AI research and development across internal products and management solutions. You will work with RL agents, segmentation, regression models, and multi-modal systems to drive product innovation.

The role emphasizes building solutions from big data insights, experimenting with self-learning agents, and deploying state-of-the-art generative models.