Roles & Responsibilities

Implement and customize cutting-edge AI research papers into working prototypes and production-ready models.
Conduct performance improvement experiments on large-scale models, including fine-tuning, prompt engineering, and architecture modifications.
Develop and manage training and evaluation datasets, including synthetic data generation, annotation pipelines, and benchmark creation.
Collaborate with engineering and product teams to deploy research outputs into scalable systems.
Stay up-to-date with the latest advancements in AI and contribute to internal research strategy.
Document experiments, share insights, and support innovation through patent filings and technical reports.

Required Skills

· Proven experience in implementing and customizing AI research papers (e.g., from arXiv, NeurIPS, ICML).

· Hands-on experience with Large Language Models (LLMs): GPT, LLaMA, Mistral, Falcon.

· Experience with Vision Models: ViT, SAM, CLIP.

· Experience with Generative Models: Stable Diffusion, DALL·E, GANs.

· Experience with Multimodal Systems combining vision, language, and audio.

· Proficiency in Python, PyTorch, TensorFlow, JAX.

· Experience with Hugging Face Transformers, Diffusers, OpenAI Gym.

· Experience with Weights & Biases, MLflow, TensorBoard.

· Experience with Distributed Training: DeepSpeed, FSDP, Horovod.

· Knowledge of Model Optimization: quantization, pruning, distillation.

· Experience with Data Tools: DVC, Apache Arrow, Label Studio.

· Strong understanding of evaluation metrics, benchmarking, and reproducibility.

Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

Highly skilled and innovative AI Research Scientist with deep expertise in implementing and customizing state-of-the-art AI research papers.
Strong hands-on capability in optimizing large-scale models and building robust training and evaluation datasets.
Experience working on real-world applications of generative AI, large language models (LLMs), and computer vision systems.
Ability to collaborate with engineering and product teams to move research outputs into scalable systems.
Strong understanding of evaluation, benchmarking, and reproducibility.