Senior Machine Learning Engineer, Large Language Models

This role at SoundHound AI involves developing and implementing innovative neural architectures and pipelines for end-to-end Automatic Speech Recognition (ASR) and large language models. The ideal candidate should have a strong foundation in AI/ML fundamentals, neural networks, transformers models, classical NLP, and hyperparameter optimization methods. They should also have a deep understanding of transformer models and associated architectures, as well as knowledge of optimization techniques for improving the inference-time performance of transformer models. Familiarity with reinforcement learning techniques in language models and prompt engineering is desired. The candidate should be up-to-date with the latest research developments in the field.

Develop and implement innovative neural architectures and pipelines for end-to-end ASR and large language models.

Innovate in developing novel approaches and architectures for integration of large language models with end-to-end ASR to support prompting.

Perform data science/analytics.

Have strong foundations in AI/ML Fundamentals.

Have strong foundations in neural networks and transformers models in particular as applied to NLP.

Have strong foundations in classical NLP.

Have knowledge of hyperparameter optimization methods such as Bayesian Optimization.

Have a deep understanding of transformer models and associated architectures.

Have knowledge of optimization techniques for improving the inference-time performance of transformer models.

Have a deep understanding of GPT (“Generative Pre-Trained Transformer”) models.

Be familiar with reinforcement learning (“RL”) techniques in LLM models (such as ChatGPT) and in particular Instruction following training methods.

Have knowledge of large language models, training validation, testing, and deployment.

Have knowledge of GPT-3, ChatGPT, and ChatGPT models.

Have knowledge of prompt engineering.

Be on top of the latest research developments in the field.

Have demonstrated ability to develop.

• Strong foundations in AI/ML fundamentals
• Knowledge of neural networks and transformer models, particularly in NLP
• Familiarity with classical NLP and hyperparameter optimization methods
• Deep understanding of end-to-end ASR and transformer models
• Knowledge of optimization techniques for improving inference-time performance of transformer models
• Familiarity with reinforcement learning techniques in LLM models, specifically in instruction following training methods
• Experience with large language models, including training, validation, testing, and deployment
• Familiarity with GPT-3, ChatGPT, and prompt engineering
• Knowledge of distributed training and associated techniques for training LLMs with large parameters
• Experience with PyTorch and TensorFlow for efficient training pipelines
• Familiarity with DeepSpeed library for distributed training
• Knowledge of ideal deployment methods and technologies in MLOps
• Experience and interest in statistical language models, such as n-gram models
• Strong foundations in probability theory and statistics, including understanding of bias/variance tradeoff and model generalization.

• Comprehensive health care
• Paid time off
• Discounted fitness classes
• Flexible working hours