Senior Machine Learning Engineer - Large Language Models
SoundHound
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Posted:
August 16, 2023
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Hybrid
About the position
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 for improving inference-time performance, and knowledge of reinforcement learning techniques. Additionally, experience with distributed training and inference, as well as proficiency in PyTorch and TensorFlow, is highly desirable. The candidate should also have a strong foundation in probability theory, statistics, and general machine learning concepts. Overall, the role requires expertise in AI/ML fundamentals, NLP, and large language models, with a focus on prompt engineering and the ability to stay up-to-date with the latest research developments in the field.
Responsibilities
- 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 knowledge of neural networks and transformer models as applied to NLP.
- Have knowledge of 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 techniques in LLM models, particularly instruction following training methods.
- Have knowledge of prompt engineering.
- Have knowledge of large language models, training validation, testing, and deployment.
- Be familiar with GPT-3, ChatGPT, and ChatGPT.
- Have knowledge of distributed training.
- Have knowledge of prompt engineering.
- Stay updated on the latest research developments in the field.
- Have the ability to develop efficient training pipelines using PyTorch.
- Have experience with distributed training and inference.
- Be familiar with associated libraries such as DeepSpeed.
- Have knowledge of ideal deployment methods and technologies and relative tradeoffs in MLOps.
- Have experience and interest in statistical language models such as n-gram models.
- Have strong foundations in probability theory and statistics.
- Understand concepts such as bias/variance tradeoff, model generalization, and bootstrapping methods.
- Have coding and implementation skills.
Requirements
- Strong foundations in AI/ML fundamentals
- Knowledge of neural networks and transformer models 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 transformer model performance
- Familiarity with GPT models and reinforcement learning techniques in LLM models
- Experience with prompt engineering
- Familiarity with large language models such as GPT-3 and ChatGPT
- Knowledge of distributed training and inference
- Experience with PyTorch (TensorFlow knowledge is desirable)
- Familiarity with libraries such as DeepSpeed
- Knowledge of MLOps and deployment methods for ML models
- Experience and interest in statistical language models
- Strong foundations in probability theory and statistics
- Coding and implementation skills
Benefits
- Comprehensive health care
- Paid time off
- Discounted fitness classes
- Flexible working hours