Applied Audio ML Engineer

About David AI David AI is the first audio data research company, bringing an R&D approach to data and developing datasets with the same rigor AI labs bring to models. Our mission is to bring AI into the real world, with audio as the gateway due to its versatility, accessibility, and human nature. As audio AI advances, high-quality training data becomes the bottleneck, which is where David AI excels. Founded in 2024 by former Scale AI engineers and operators, David AI has quickly secured most FAANG companies and AI labs as customers. We recently raised a $50M Series B from top-tier investors including Meritech, NVIDIA, Jack Altman (Alt Capital), Amplify Partners, and First Round Capital. Our team is sharp, humble, ambitious, and tight-knit. We are seeking the best research, engineering, product, and operations minds to join us in pushing the frontier of audio AI. About our Machine Learning team Our Machine Learning team operates at the intersection of cutting‑edge research and production systems, converting raw audio into high‑signal data for leading AI labs and enterprises. We manage the complete ML lifecycle, from researching novel speech processing algorithms to deploying models that process terabytes of audio daily. About this role As an Applied ML Engineer at David AI, you will be instrumental in building advanced speech and audio models, production inference systems, and resilient pipelines that demonstrate the power of high‑quality data. In this role, you will Research, design, and implement solutions utilizing advanced signal processing algorithms and bleeding‑edge ML models with applications to speech and audio. Develop production‑grade inference algorithms, pipelines, and APIs in collaboration with cross‑functional teams to unlock key insights into our data for customers. Collaborate with our Operations team to gather useful training and evaluation datasets to enhance the quality of our models. Architect systems that ensure resilient and 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. #J-18808-Ljbffr David AI