Staff Software Engineer, ML

Job Description:

Our Products Team is growing and were looking for a highly skilled Senior Machine Learning Engineer to join our cutting-edge Generative AI 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: 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:

• Bachelors Masters or Ph.D. in Computer Science Machine Learning Artificial Intelligence Data Science or a related field.

• 5 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.

• Strong hands-on experience with deep learning frameworks such as TensorFlow PyTorch or JAX.

• Strong 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.

• Strong 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

• 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