Posted on: 07/09/2025
About Us :
Key Responsibilities :
- Conduct Research : Drive independent and collaborative research in AI, with a focus on advancing state-of-the-art algorithms in areas such as natural language processing, computer vision, reinforcement learning, or deep learning.
- Algorithm Development : Design and develop innovative machine learning models and algorithms to solve complex problems, pushing the envelope in terms of performance and efficiency.
- Experimentation & Evaluation : Plan, implement, and evaluate experiments to validate research hypotheses. Analyze large datasets to derive insights and improve models.
- Collaboration : Collaborate with cross-functional teams including data scientists, engineers, and product managers to translate research findings into production-ready AI systems.
- Documentation & Publishing : Document research findings and methodologies, and contribute to publications in top-tier AI/ML conferences and journals.
- Innovation : Stay up-to-date with the latest trends and developments in AI research. Evaluate new techniques and integrate them into the organizations AI capabilities.
Required Qualifications :
- Education : Masters or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
Experience :
- 2+ years (for entry-level) or 5+ years (for senior) of research experience in AI/ML.
- Proven track record of academic or industry contributions to AI, such as publications in reputable conferences (NeurIPS, CVPR, ICML, etc.) or successful implementation of AI models in production environments.
Technical Skills :
- Proficiency in Python and deep learning frameworks (TensorFlow, PyTorch, etc.).
- Strong understanding of machine learning algorithms, statistics, and optimization techniques.
- Experience working with large-scale datasets and distributed computing environments.
- Research Experience :
Demonstrated expertise in one or more areas of AI, including but not limited to :
- Natural Language Processing (NLP)
- Computer Vision (CV)
- Reinforcement Learning
- Generative Models (e.g., GANs, VAEs)
- Multi-Agent Systems
- Graph Neural Networks (GNNs)
- Tools & Libraries : Experience with ML/DL tools such as scikit-learn, Keras, OpenCV, Hugging Face, etc.
- Communication : Strong ability to present complex research findings to both technical and non-technical audiences. Excellent written and verbal communication skills.
Preferred Qualifications :
- Industry Experience : Prior experience in applying AI research in industry (e.g., healthcare, autonomous vehicles, robotics, etc.).
- Open Source Contributions : Active contributions to AI-related open-source projects or libraries.
- Advanced Topics : Exposure to advanced topics such as meta-learning, self-supervised learning, or AI ethics.
- Cloud Platforms : Experience with cloud computing platforms like AWS, GCP, or Azure for large-scale model training and deployment
The job is for:
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