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Open nowFirst seen 6 days ago

Applied Audio ML Engineer sf, ca

ESRhealthcare and EXEC STAFF RECRUITERS1,405 open roles

Pay
$190,000 – $260,000 a year
Where
San Francisco, California, United States
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Your applicationOpen nowApplied Audio ML Engineer sf, caESRhealthcare and EXEC STAFF RECRUITERS · San Francisco, California, United States

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: first seen 6 days ago

The posting

Applied Audio ML Engineer

Location

San Francisco

Employment Type

Full time

Location Type

On-site

Department

Engineering

Compensation

$190K – $260K Offers Equity

About this role

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.

In this role, 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.

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

Architect systems that enable resilient, durable inference and evaluations.

Your background looks like

5+ years of professional audio ML experience, including DSP and ML audio algorithm development.

End-to-end ownership of ML pipelines, from proof-of-concept to production deployment.

Strong coding skills in Python and proficiency with deep learning frameworks such as PyTorch.

Ability to translate research papers and ideas into high-quality, production-ready code.

Experience deploying ML systems for production inference with cloud technologies.

Track record of setting ML roadmaps, influencing technical direction, and prioritizing research and infrastructure investments.

Ability to assess model quality in the context of user experience and business value.

Bonus points if you have

PhD or Masters in Computer Science or a related field.

Experience training generative AI models.

Expertise in audio signal processing both classical and machine learning techniques.

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