Founding Applied ML Engineer

David AI; United States; Remote; USD 80,000 - 140,000; Full time

Job summary

An innovative company is seeking an Applied ML Engineer to develop cutting-edge speech and audio models. In this role, you will leverage advanced signal processing and machine learning techniques to create production-grade inference systems and resilient pipelines. You'll collaborate with cross-functional teams to unlock insights from data, driving the ML roadmap and influencing technical direction. This position offers the autonomy to lead research and infrastructure investments in a dynamic environment. If you're passionate about pushing the boundaries of technology and making a significant impact, this opportunity is perfect for you.

Qualifications

• Experience with advanced signal processing and machine learning techniques.
• Ability to work collaboratively in cross-functional teams.

Responsibilities

• Research and implement solutions using advanced signal processing algorithms.
• Develop production-grade inference algorithms and resilient pipelines.

Skills

Signal Processing Algorithms

Machine Learning Models

API Development

Data Analysis

Education

Bachelor's Degree in Computer Science or related field

Master's Degree in Machine Learning or related field

Tools

TensorFlow

PyTorch

Docker

Job description

As an Applied ML Engineer at David AI you'll build cutting-edge speech and audio models, production inference systems and resilient pipelines that showcase what high-quality data can really do.

You will:

• Research, design, and implement solutions using advanced signal processing algorithms and bleeding edge ML models with application to speech and audio.

• Develop production-grade inference algorithms, pipelines, and APIs with cross-functional teams that unlock key insights into our data for our customers.

• Collaborate with our Operations team to gather useful training and evaluation datasets to improve the quality of our models.

• Architect the systems that enable resilient, durable inference and evaluations.

• Have full autonomy to drive the ML roadmap, influence technical direction, and lead research and infrastructure investments.