Principal Software Engineer, AI/ML

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.

Calix is where passionate innovators come together with a shared mission: to reimagine broadband experiences and empower communities like never before. As a true pioneer in broadband technology, we ignite transformation by equipping service providers of all sizes with an unrivaled platform, state-of-the-art cloud technologies, and AI-driven solutions that redefine what’s possible. Every tool and breakthrough we offer is designed to simplify operations and unlock extraordinary subscriber experiences through innovation.

Our Products Team is growing, and we're looking for a highly skilled Principal Software Engineer, AI/ML, to join our cutting-edge AI / ML platform team. In this role, you will play a key part in designing, developing, and deploying advanced AI/ML 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/ML 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, V AEs, 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 SME, 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:

• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, AI, Data Science, or a related field.

• Experience: 12+ years in cloud software development and 5+ years specializing in AI and Machine Learning

• Technical Expertise:

• Generative AI models (e.g., GPT, VAEs, GANs, Transformer architectures)

• Deep learning frameworks: TensorFlow, PyTorch, JAX

• Programming:

• Python (NumPy, Pandas, Scikit-learn), plus Java, Go, C/C++, R

• NLP, image generation, and multimodal models

• Training and fine-tuning large-scale models (e.g., GPT, BERT, DALL-E)

• Cloud platforms (AWS, GCP, Azure) and ML Ops (Docker, Kubernetes)

• Data engineering and large-scale dataset handling

• SQL and NoSQL databases

• Development Practices:

• Strong coding standards

• Testing and CI/CD pipelines

Soft Skills:

• Ability to mentor and lead system design

• Excellent problem-solving, collaboration, and communication

• Proactive in learning and adopting 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

The base pay range for this position varies based on the geographic location. More information about the pay range specific to candidate location and other factors will be shared during the recruitment process. Individual pay is determined based on location of residence and multiple factors, including job-related knowledge, skills and experience.

San Francisco Bay Area:

182,900 - 310,500 USD Annual159,000 - 270,000 USD Annual