Job Summary
We are seeking a highly motivated and skilled Research Engineer with 2+ years of experience in AI/ML to join our dynamic R&D team. The ideal candidate is passionate about applied machine learning, has strong algorithmic thinking, and possesses excellent communication skills. The role offers an exciting opportunity to work on cutting-edge research and contribute to real-world applications.
Key Responsibilities
- Design, develop, and optimize algorithms in AI/ML for real-world applications.
- Conduct research and experiments to solve open-ended engineering problems.
- Collaborate with cross-functional teams to define problem statements and develop technical solutions.
- Prototype, validate, and scale AI/ML models using Python and related frameworks.
Contribute to technical documentation, research papers, and presentations.
- Stay up-to-date with the latest developments in machine learning and related fields.
- Develop and enhance rendering techniques to produce photorealistic images and animations, utilizing advanced shading, lighting, and texturing methods
Required Qualifications
- 2+ years of hands-on experience in AI/ML, data science, or related areas. Deep learning, machine learning, recurrent learning, 3D CNNs, foundation models (good to have)
- Hands on conventional and AI-based Image processing.
- Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Optimization and pruning of AI algorithms
- Knowledge of Git
Educational Requirements
- Completed or currently in the final stages of a Ph.D. program in one of the following disciplines:
- Medical Electronics
- Biomedical Engineering
- Computational data science
- Computer Science
- Artificial Intelligence / Machine Learning
- Electronics or Electrical Engineering
- Information Science
- Related engineering fields
Preferred Qualifications
- Experience with research publication, internships, or projects in AI/ML.
- Exposure to domain-specific AI applications (e.g., healthcare, signal/image processing, embedded AI).
- Familiarity with scientific computing tools and environments.
