Research and Development Scientist
5 days ago
We're looking for a talented Machine Learning Research Engineer to join our team at Phitopolis. As a Machine Learning Research Engineer, you'll play a key role in driving innovation and advancing the field of AI and ML.
About the RoleThis is an excellent opportunity to contribute to the development of cutting-edge AI and ML solutions. If you're passionate about AI, ML, and computer vision, this could be the perfect fit.
The successful candidate will have a strong background in machine learning, experience with transformer-based architectures, and a proven track record of designing and implementing state-of-the-art models.
Key Responsibilities:
- Model Development: Design and implement state-of-the-art machine learning models, including transformer-based LLMs, GANs, diffusion models, and RNNs (e.g., LSTMs), tailored to computer vision and sensor data.
- Predictive Modeling: Develop predictive models for time series analysis from sensor and camera data.
- Cutting-Edge Research: Explore cutting-edge techniques in generative AI and predictive analytics to solve real-world challenges in computer vision.
- Research Publication: Publish research findings internally and integrate them into production-ready solutions.
- Data Analysis and Processing: Process, clean, and analyze large datasets collected from computer vision systems, cameras, and other sensors.
- Collaboration and Mentorship: Work closely with a multidisciplinary team of engineers and researchers to integrate AI models into larger systems and provide technical mentorship and guidance to junior engineers.
Requirements:
Technical Expertise
- Transformer-Based Architectures: Proven experience with transformer-based architectures (e.g., GPT, BERT, Vision Transformers).
- Generative ML Techniques: Hands-on expertise in generative ML techniques, including GANs, VAEs, and diffusion models.
- Predictive Analytics: Proficiency in predictive analytics using LSTMs and advanced time series analysis.
- Computer Vision Principles: Solid understanding of computer vision principles and experience with vision datasets.
Programming & Tools
- Python and Standard Frameworks: Proficiency in Python and standard machine learning frameworks (PyTorch, TensorFlow).
- Libraries and Platforms: Familiarity with libraries like Hugging Face, OpenCV, and NumPy.
- Cloud Platforms: Experience with cloud platforms (e.g., AWS, Azure, GCP) for model training and deployment.
Education
- Bachelor's or Master's Degree: Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related fields.
- Ph.D. or Published Research: Ph.D. or published research in a relevant domain is a big plus.
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