Applied Audio ML Engineer
San Francisco, CAFull-time$150–260K/yrSeen 2mo agoStill listed 6 days ago
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At a glance
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Job overview
The Machine Learning team at David AI transforms raw audio into high‑signal data, handling the full ML lifecycle from research to production. As an Applied Audio ML Engineer, you will design advanced speech and audio models, build production inference systems, and create resilient pipelines that deliver valuable insights for customers.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
About our Machine Learning team Our Machine Learning team sits at the intersection of cutting-edge research and production systems, transforming raw audio into high-signal data for leading AI labs and enterprises. We own the full ML lifecycle - from researching novel speech processing algorithms to deploying models processing terabytes of audio daily. 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.
Compensation and benefits
- Rapid career growth at one of the fastest growing Series A companies, within a new and booming industry.
- Competitive salary and equity package.
- Flexible PTO policy.
- Top-notch health, dental, and vision coverage with 100% company reimbursement for most plans.
- Paid lunch and dinner in the office, every day through DoorDash.
- 401k access.
How this job compares
Set against the 6,068 open Machine Learning Engineer jobs on Olive Jobs, counted November 2, 2026.
- Pay
- This posting$150–260K/yr
- California median$176–261K/yr1,359 salary ranges
- US median$150–245K/yr4,824 salary ranges
This range's midpoint, $205K, is 7% below the California median midpoint ($222K) — in the middle half of Machine Learning Engineer salary ranges in California.
- Openings
- Olive Jobs lists 1,837 open Machine Learning Engineer jobs in California and 6,068 across the US. 8% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 3 years of experience; Machine Learning Engineer postings that state a minimum ask for 5 years at the median (4,863 postings).
- Education
- 2,299 of the 6,068 open Machine Learning Engineer jobs (38%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 2, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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