Senior Machine Learning Engineer - Large Language Models

SoundHound AI is seeking a candidate for a role focused on 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 deep understanding of transformer models, optimization techniques, and reinforcement learning techniques. They should also be familiar with prompt engineering and have knowledge of distributed training. The candidate should be up-to-date with the latest research developments in the field and have experience in developing efficient training pipelines using PyTorch.

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 prompt engineering.

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

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

Have knowledge of distributed training.

Have the ability to develop efficient training pipelines using PyTorch (TensorFlow knowledge and experience is highly desirable).

Have experience with distributed training.

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

• Strong foundations in AI/ML fundamentals
• Knowledge of neural networks and transformer models, particularly in NLP
• Familiarity with classical NLP
• Experience with hyperparameter optimization methods, such as Bayesian Optimization
• Deep understanding of end-to-end ASR and transformer models
• Knowledge of optimization techniques for improving the inference-time performance of transformer models
• Familiarity with GPT models and reinforcement learning techniques in LLM models
• Experience with prompt engineering
• Familiarity with large language models, including GPT-3 and ChatGPT
• Knowledge of distributed training and inference, including model and data parallelism
• Experience with PyTorch (TensorFlow knowledge is a plus)
• Familiarity with libraries such as DeepSpeed
• 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
• Coding and implementation skills

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