Senior Machine Learning Engineer, Large Language Models

This role is available throughout the United States. The workplace options for this role include in-office or hybrid (we have an office in Santa Clara, CA), or virtual/remote. The salary range for this role is between $142,000 to $195,000. The recruiting team will provide a more specific range based on location and years of experience. In addition to salary, total compensation for this role includes equity (RSUs), comprehensive health care, paid time off, a flexible workplace model, and other benefits.

In this role, you will:

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

We would love to hear from you if:

You have strong foundations in the following areas

AI/ML Fundamentals

Neural networks and transformers models in particular as applied to NLP
Classical NLP
Hyperparameter optimization methods such as Bayesian Optimization

End-To-End-ASR

Deep understanding of transformer models and associated architectures

Knowledge of optimization techniques for improving the inference-time performance of transformer models is highly desirable
Deep understanding of GPT (“Generative Pre-Trained Transformer”) models

Familiarity with reinforcement learning (“RL”) techniques in LLM models (such as ChatGPT) and in particular Instruction following training methods such as those described in the InstructGPT paper

Prompt engineering

Large Language Models, training validation, testing and deployment

GPT-3, ChatGPT, ChatGPT

Knowledge of distributed training is highly desirable
Prompt Engineering
The ideal candidate will be on top of the latest research developments in the field.

Demonstrated ability to develop efficient training pipelines using PyTorch (TensorFlow knowledge and experience is highly desirable)
Experience with distributed training and inference (Model and Data parallelism) and the associated techniques necessary to train LLMs having tens of billions to 100s of billions of parameters

E.g., familiarity with associated libraries such as DeepSpeed is highly desirable
Knowledge of ideal deployment methods and technologies and relative tradeoffs MLOps

General ML, Statistics and Data Science

Experience and interest in statistical language models (“SLMs”) such as n-gram models is highly desirable
Strong foundations in probability theory and statistics and understanding of concepts such as:

bias/variance tradeoff
model generalization
bootstrapping methods

Coding and Implementation

Demonstrated facility with PyTorch (TensorFlow knowledge and experience is highly desirable
Ability to write clean code in Python with strong design skills leveraging design patterns and object-oriented methods
C/C++ coding skills are a strong plus

We’d be especially excited if you have a background with the following:

Whisper model
Wave2Vec model and other self-supervised approaches
Transducer models such as RNN-T
Shallow fusion models
Transducer Models
DeepSpeed
LoRA
Maestro

Here at SoundHound, diversity, equity, and inclusion are key to who we are as a company. With a mission to build Voice AI for the world, creating a team with global perspectives is critical to our success.

We care deeply about fostering an environment where everyone is supported and can do their best work. SoundHound ensures that individuals with disabilities are provided reasonable accommodations to participate in the interview process, perform essential job functions, and receive other employment benefits.

Employees enjoy comprehensive health care, paid time off, discounted fitness classes, and flexible working hours. Learn more about our philosophy, benefits, and culture at https://soundhound.com/careers/.

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Come join our growing team and bring your unique voice to our mission!