Staff Software Engineer, AI/ML

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: 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.