Senior AI Research Engineer — Model R&D & Innovation

Flatgigs; Islamabad; On-site; PKR 1,800,000 - 3,600,000; Full time

Job summary

Flatgigs is seeking a Senior Applied AI Research Engineer to join our R&D team. You will conduct hands-on AI research across computer vision, NLP, LLMs, RL, and advanced deep learning techniques, translating cutting-edge ideas into functional prototypes.

You will design experiments, train and optimize models in PyTorch or TensorFlow, develop training pipelines, benchmark performance, and collaborate with researchers and software engineers to move concepts from paper to production.

Qualifications

• Minimum 5 years of hands-on AI/ML research and model development.
• Experience translating research into prototypes and production-ready code.
• Strong proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX).
• Familiarity with CV, NLP, and RL concepts and evaluation methodologies.
• Ability to read research papers and implement methodologies.

Responsibilities

• Conduct applied AI research in AI/ML/DL, CV, NLP, and emerging AI technologies.
• Review, reproduce, and implement methodologies from academic papers and open-source projects.
• Design and execute experiments to evaluate models, architectures, and training methodologies.
• Benchmark, perform ablation studies, and document results.
• Translate research findings into functional prototypes and reusable components.
• Collaborate with researchers, ML engineers, and software teams to apply research to real-world problems.

Skills

Python

PyTorch

TensorFlow

Model optimization

Experiment design

Git

Education

Bachelor's or Master's in CS/AI/ML/Robotics

Tools

JAX

Hugging Face Transformers

Git

Job description

Flatgigs is seeking a Senior Applied AI Research Engineer to join our R&D team. You will conduct hands-on AI research across computer vision, NLP, LLMs, RL, and advanced deep learning techniques, translating cutting-edge ideas into functional prototypes.

You will design experiments, train and optimize models in PyTorch or TensorFlow, develop training pipelines, benchmark performance, and collaborate with researchers and software engineers to move concepts from paper to production.