David AI
Applied Audio ML Engineer
New
150000 –260000 USD / yearUSASeniorpythonpytorchsignal processing
Build and deploy cutting-edge speech and audio machine learning models and production systems.
Responsibilities
- 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.
Nice to 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.
Conditions
- 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.
Other
- 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.
- 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.
- 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.