Staff 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: Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field. 5+ years focus on Machine Learning. 10+ years overall software engineering in production Proven experience with generative AI models such as GPT, VAEs, GANs, or Transformer architectures. Strong hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Expertise in Python and libraries such as NumPy, Pandas, Scikit-learn. Experience with Natural Language Processing (NLP) , image generation , or multimodal models . Familiarity with training and fine-tuning large-scale models (e.g., GPT, BERT, DALL-E). Knowledge of cloud platforms (AWS, GCP, Azure) and ML ops pipelines (e.g., Docker, Kubernetes) for deploying machine learning models. Strong background in data manipulation, data engineering , and working with large datasets. Strong coding experience in Python , Java, Go, C/C++, R  Good data skills – SQL, Pandas, exposure to various SQL and no SQL data bases Solid development experience with dev cycle on Testing and CICD Strong problem-solving abilities and attention to detail. Excellent collaboration and communication skills to work effectively within a multidisciplinary team. Proactive approach to learning and exploring new AI technologies. Preferred Skills: Experience with Reinforcement Learning or Self-Supervised Learning in generative contexts. Familiarity with distributed training and high-performance computing (HPC) for scaling large models. Contributions to AI research communities or participation in AI challenges and open-source projects. Tools: Linux, git, Jupyter, IDE, ML frameworks: Tensorflow, Pytorch, Keras, Scikit-learn GenAI: prompt engineering, RAG pipeline, Vector/Graph DB, evaluation frameworks, model safety and governance Location: India – (Flexible hybrid work model - work from Bangalore office for 20 days in a quarter)