Applied AI Research Scientist (PhD / Advanced Research & Competitive ML)

About the Role

We are seeking an Applied AI Research Scientist with a strong research background (PhD or equivalent experience) and a proven track record in solving complex machine learning problems, including participation in competitive platforms such as Kaggle.

This role is ideal for individuals who thrive on tackling hard problems, experimenting with novel approaches, and translating high-performance models into real-world applications. You will work on cutting-edge AI challenges, combining rigorous research thinking with practical implementation.

Key Responsibilities

• Develop and implement advanced machine learning models across domains such as NLP, computer vision, time series, or generative AI
• Translate research ideas, competition-winning approaches, and academic insights into scalable production solutions
• Design and run experiments with strong statistical rigor, including feature engineering, model tuning, and ensemble techniques
• Leverage competition-style problem solving (e.g., Kaggle) to push model performance and benchmark against state-of-the-art
• Optimize models for accuracy, generalization, and efficiency in real-world environments
• Collaborate cross-functionally with engineering and product teams to deploy and monitor models in production
• Stay at the forefront of AI research and incorporate relevant advancements into applied systems
• Document methodologies and contribute to knowledge sharing, publications, or open-source initiatives where applicable

Required Qualifications

• PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field
• OR Master’s with equivalent hands-on experience in advanced ML problem solving
• Strong foundation in machine learning theory, statistics, and optimization
• Proven experience building high-performing models in real-world or competitive environments
• Hands-on expertise in Python and ML frameworks such as PyTorch, TensorFlow, or JAX
• Experience with end-to-end ML workflows: data processing, modeling, evaluation, and deployment
• Demonstrated ability to handle ambiguous problems and derive actionable insights from complex datasets

Preferred Qualifications

• Active participation in ML competitions (e.g., Kaggle, DrivenData, Zindi), with strong rankings (e.g., top 10%, medals, or Grandmaster/Master status)
• Publications in top-tier conferences (NeurIPS, ICML, ICLR, ACL, CVPR, etc.)
• Experience with advanced techniques such as ensembling, feature engineering at scale, hyperparameter optimization, and model stacking
• Familiarity with large language models (LLMs), generative AI, or foundation models
• Experience with MLOps, model deployment, and cloud platforms (AWS, GCP, Azure)
• Knowledge of distributed training and large-scale data systems

Key Skills

• Advanced Machine Learning & Deep Learning
• Experimental Design & Statistical Analysis
• Feature Engineering & Model Optimization
• Competitive Problem Solving (Kaggle-style)
• Research-to-Production Translation
• Programming (Python, SQL, ML frameworks)
• Communication of complex technical ideas