Applied Scientist

About the Role

We are seeking a driven Applied Scientist to bridge the gap between cutting-edge Machine Learning (ML) research and practical product implementation. In this role, you will design, build, and deploy scalable ML, Deep Learning, and Generative AI models that power core product features, improve system intelligence, and deliver measurable impact to our users.

Key Responsibilities

1. Model Development: Design, train, and fine-tune machine learning and deep learning models (including LLMs, computer vision, or predictive analytics) to solve complex real-world problems.
2. Production Deployment: Partner with ML Engineering teams to convert prototypes into production-grade pipelines, optimizing for latency, throughput, and scalability.
3. Experimentation & Optimization: Conduct rigorous statistical analysis, A/B testing, and continuous model performance evaluation to drive iterative enhancements.
4. Technical Leadership: Translate business requirements into actionable technical roadmap initiatives and publish reproducible code and insights.
5. Research & Innovation: Stay current with modern state-of-the-art AI trends, incorporating relevant open-source models, frameworks, and techniques into system architecture.

Qualifications

1. Education: Master’s degree or Ph.D. in Computer Science, Machine Learning, Applied Mathematics, Data Science, or a related quantitative field.
2. Programming & ML Stack: Proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
3. Production Experience: Strong experience with SQL, Docker, cloud environments (AWS, GCP, or Azure), and end-to-end MLOps/LLMOps tools.
4. Core Competencies: Deep understanding of data structures, algorithms, model evaluation metrics, and feature engineering techniques.