Senior Machine Learning Engineer

Calix provides the cloud, software platforms, systems and services required for communications service providers to simplify their businesses, excite their subscribers and grow their value.

This is a remote-based position that can be located anywhere in the United States or Canada.

Our Products Team is growing and we're looking for a highly skilled Senior Machine Learning Engineer to join our cutting-edge Generative AI project. In this role, you will play a key part in designing, developing, and deploying advanced AI models focused on content generation, natural language understanding, and creative data synthesis. You will work alongside a team of data scientists, software engineers, and AI researchers to build systems that push the boundaries of what generative AI can achieve.

Key Responsibilities :

• Design and Build ML Models : Develop and implement advanced machine learning models (including deep learning architectures) for generative tasks, such as text generation, image synthesis, and other creative AI applications.
• Optimize Generative AI Models : Enhance the performance of models like GPT, VAEs, GANs, and Transformer architectures for content generation, making them faster, more efficient, and scalable.
• Data Preparation and Management : Preprocess large datasets, handle data augmentation, and create synthetic data to train generative models, ensuring high-quality inputs for model training.
• Model Training and Fine-tuning : Train large-scale generative models and fine-tune pre-trained models (e.g., GPT, BERT, DALL-E) for specific use cases, using techniques like transfer learning, prompt engineering, and reinforcement learning.
• Performance Evaluation : Evaluate models’ performance using various metrics (accuracy, perplexity, FID, BLEU, etc.), and iterate on the model design to achieve better outcomes.
• Collaboration with Research and Engineering Teams : Collaborate with cross-functional teams including AI researchers, data scientists, and software developers to integrate ML models into production systems.
• Experimentation and Prototyping : Conduct research experiments and build prototypes to test new algorithms, architectures, and generative techniques, translating research breakthroughs into real-world applications.
• Deployment and Scaling : Deploy generative models into production environments, ensuring scalability, reliability, and robustness of AI solutions in real-world applications.
• Stay Up-to-Date with Trends : Continuously explore the latest trends and advancements in generative AI, machine learning, and deep learning to keep our systems at the cutting edge of innovation.

Qualifications :

Preferred Skills :

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