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Hybrid Machine Learning Engineer

micro1 · APAC (Remote)  ·  nan, Australie · Hybrid

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About the job

Job Title: Machine Learning Engineer

Job Type: Full-Time

Location: Remote

Job Summary:

Join our dynamic team as a Machine Learning Engineer, where you'll lead the charge in developing cutting-edge ML models and systems. This role offers the opportunity to leverage your expertise in Python, AWS, and C++ in an innovative, remote work environment. Collaborate on transformative projects at the intersection of AI and cloud technology, shaping the future of intelligent systems.

Key Responsibilities:

• Design, develop, and optimize machine learning models and algorithms using Python and TensorFlow.

• Collaborate with cross-functional teams to integrate AI solutions into existing systems.

• Implement high-performance computing techniques using C++ and CUDA for deep learning tasks.

• Manage cloud infrastructure on AWS, ensuring scalable and secure deployment of ML applications.

• Conduct research and experiments with PyTorch and JAX to advance ML capabilities.

• Write clear and comprehensive documentation, showcasing your proficiency in written communication.

• Maintain and enhance existing models, ensuring accuracy and efficiency in outputs.

Required Skills and Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

• 5+ years of experience in machine learning and software engineering.

• Proficiency in Python and C++ programming languages.

• Extensive experience with AWS and cloud service providers such as Azure and GCP.

• Strong understanding of machine learning frameworks like TensorFlow, PyTorch, and Hugging Face Transformers.

• Excellent written and verbal communication skills.

• Experience with GPU acceleration technologies such as CUDA.

Preferred Qualifications:

• Familiarity with JAX and its use in high-performance numerical computing.

• Experience with deploying AI models in web-based environments using JavaScript.

• Proven track record of published research or contributions to open-source ML projects.

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