Calix

StaffSoftwareEngineer,AI/ML

Bangalore, Karnataka, India FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Staff Software Engineer, AI/ML at Calix. Skills: Generative AI, Machine Learning, Deep Learning, Python. Design and Build ML Models. Optimize Generative AI Models”

What You'll Achieve.

achieve better outcomes; deploy generative models into production environments; ensuring scalability, reliability, and robustness of AI solutions

Industry & Context.

Problems you'll solve

problem-solving abilities

What They're Looking For.

Must Have

Bachelor’s, Master’s, or Ph. D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field, 8+ years of overall software engineering in production, 3-5+ years of focus on Machine Learning, Proven experience with generative AI models such as GPT, V AEs, GANs, or Transformer architectures, hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX, coding experience in Python, Java, Go, C/C++, R, Expertise in Python and libraries such as NumPy, Pandas, and 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, background in data manipulation, data engineering, and working with large datasets, Good data skills - SQL, Pandas, exposure to various SQL and non-SQL databases, Solid development experience with dev cycle on Testing and CICD 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

Nice to Have

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

What You'll Do.

Design and Build ML Models

Optimize Generative AI Models

Data Preparation and Management

Model Training and Fine-tuning

Performance Evaluation

Collaboration with Research and Engineering Teams

Experimentation and Prototyping

Deployment and Scaling

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How You'll Work.

Team & Collaboration

Collaborate with cross-functional teams, including AI researchers, data scientists, and software developers; Work alongside a team of data scientists, software engineers, and AI researchers; work effectively within a multidisciplinary team

Communication Scope

Excellent collaboration and communication skills

Full Job Description

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. Job Description Our Products Team is growing, and we're looking for a highly skilled Senior Software Engineer, AI/ML, to join our AI/PL platform team. 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, 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 AI researchers, data scientists, and software developers, to integrate ML models into production systems. * Experimentation and Prototyping: C

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