Junior AI Researcher

We are a distributed team of innovators and problem-solvers dedicated to pushing the boundaries of what's possible with artificial intelligence. Our culture is built on a foundation of intellectual curiosity, rigorous scientific inquiry, and deep collaboration. You will join an environment that encourages continuous learning and provides the autonomy to explore novel ideas and challenge conventional thinking. Your work here will directly contribute to meaningful advancements in a variety of impactful fields.

Key Responsibilities

- Design and execute novel experiments to advance our team's understanding of machine learning and deep learning algorithms.

- Perform comprehensive data collection, preprocessing, and augmentation to build robust, high-quality datasets for model training and validation.

- Rigorously test, validate, and benchmark AI models (including those in Natural Language Processing and Computer Vision) to ensure accuracy, efficiency, and scalability.

- Implement and code research experiments using Python and its ecosystem of libraries—including Pandas, NumPy, and Scikit-learn.

- Develop, train, and fine-tune models within major deep learning frameworks like TensorFlow and PyTorch.

- Meticulously document your entire research process, methodologies, and experimental results for internal review and future publication.

- Conduct thorough literature reviews to stay at the forefront of AI research trends, emerging techniques, and academic breakthroughs.

- Apply advanced statistical analysis and data visualization techniques to interpret model outputs and derive actionable, data-driven insights.

- Collaborate effectively with senior researchers, engineers, and product managers to translate research concepts into tangible, innovative solutions.

- Contribute to the preparation of research findings for submission to top-tier academic conferences and scientific journals.

Qualifications and Skills

- A Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field is required.

- A strong foundational understanding of the scientific method and research principles, ideally demonstrated through academic projects, publications, or internships.

- Proficiency in Python for data science and machine learning, with hands-on experience using libraries such as Pandas, NumPy, and Matplotlib.

- Solid theoretical knowledge and practical experience with core machine learning concepts—from regression and classification to neural networks and ensemble methods.

- Experience with at least one major deep learning framework, such as TensorFlow, PyTorch, or Keras, is essential.

- Demonstrated problem-solving skills with the ability to tackle complex, ambiguous challenges with creativity and analytical rigor.

- Excellent communication and documentation skills, capable of clearly articulating complex technical concepts to both technical and non-technical stakeholders.

- Familiarity with version control systems (especially Git and GitHub) for collaborative code and project management.