Junior ML Research Engineer
We are a distributed team of scientists and engineers dedicated to solving some of the most challenging problems through applied artificial intelligence. Our culture is built on a foundation of intellectual curiosity, rigorous scientific method, and deep collaboration. We empower our team members to take ownership of their research, explore unconventional ideas, and grow their expertise in a supportive and intellectually stimulating environment. You will be joining a group that values continuous learning and tangible impact above all else.
What You Will Do
- Research and evaluate state-of-the-art machine learning and deep learning algorithms to solve complex, real-world problems.
- Design and implement robust experimental frameworks in Python to rigorously test novel hypotheses and concepts.
- Develop, train, and fine-tune machine learning models using leading libraries like TensorFlow, PyTorch, and scikit-learn.
- Analyze and interpret intricate datasets to extract actionable insights and validate model performance metrics with scientific precision.
- Debug and troubleshoot issues across the entire ML pipeline—from data ingestion to model deployment—ensuring system reliability.
- Collaborate closely with senior researchers and engineers in a dynamic, agile environment to integrate your findings into larger systems.
- Document your research process, experimental results, and code with exceptional clarity to support knowledge sharing and reproducibility.
- Contribute to the preparation of findings for internal reports and potentially for submission to top-tier academic conferences and journals.
Core Qualifications
- A Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field is required.
- Demonstrable project or academic experience with Python for machine learning and data analysis tasks.
- A solid theoretical understanding of core machine learning concepts (e.g., classification, regression, clustering, neural networks).
- Hands-on experience with at least one major ML framework, such as TensorFlow, PyTorch, or Keras.
- Proficiency in using essential data science libraries, including Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for visualization.
- Exceptional problem-solving and analytical skills, characterized by a meticulous attention to detail and a creative approach to challenges.
- Strong written and verbal communication skills, which are crucial for documenting your work and presenting complex research findings to a technical audience.
- A proactive and collaborative mindset with the proven ability to work effectively and autonomously in a remote team environment.
Preferred Qualifications
- Experience with cloud platforms (AWS, GCP, Azure) and their associated ML services is a significant plus.
- Foundational knowledge of a specialized domain, such as natural language processing (NLP) or computer vision (CV).
- A public portfolio of personal ML projects (e.g., on GitHub) or contributions to open-source software.
- Previous research experience gained through an internship, a research assistantship, or contributions to academic papers.