Transformative Solutions Developer

5 days ago

Taguig, National Capital Region, Philippines Phitopolis Full time

We're seeking a talented Machine Learning Research Engineer to join our team at Phitopolis. This is an exciting opportunity to contribute to the development of cutting-edge AI and ML solutions.

About the Role

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.

The Machine Learning Research Engineer will play a key role in driving innovation and advancing the field of AI and ML. If you're passionate about AI, ML, and computer vision, this could be the perfect fit.

Key Responsibilities:

  1. 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.
  2. Predictive Modeling: Develop predictive models for time series analysis from sensor and camera data.
  3. Cutting-Edge Research: Explore cutting-edge techniques in generative AI and predictive analytics to solve real-world challenges in computer vision.
  4. Research Publication: Publish research findings internally and integrate them into production-ready solutions.
  5. Data Analysis and Processing: Process, clean, and analyze large datasets collected from computer vision systems, cameras, and other sensors.
  6. 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

  1. Transformer-Based Architectures: Proven experience with transformer-based architectures (e.g., GPT, BERT, Vision Transformers).
  2. Generative ML Techniques: Hands-on expertise in generative ML techniques, including GANs, VAEs, and diffusion models.
  3. Predictive Analytics: Proficiency in predictive analytics using LSTMs and advanced time series analysis.
  4. Computer Vision Principles: Solid understanding of computer vision principles and experience with vision datasets.

Programming & Tools

  1. Python and Standard Frameworks: Proficiency in Python and standard machine learning frameworks (PyTorch, TensorFlow).
  2. Libraries and Platforms: Familiarity with libraries like Hugging Face, OpenCV, and NumPy.
  3. Cloud Platforms: Experience with cloud platforms (e.g., AWS, Azure, GCP) for model training and deployment.

Education

  1. Bachelor's or Master's Degree: Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related fields.
  2. Ph.D. or Published Research: Ph.D. or published research in a relevant domain is a big plus.

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