About the job
Our team is a fast-growing group of committed researchers and engineers. The mission of the team is to build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. As an engineer on this team, you will work on advancing core audio model serving metrics, including latency, throughput, and quality by diving deep into our systems, identifying bottlenecks, and delivering creative solutions for audio processing and streaming workloads. You’ll collaborate closely with both the training and serving infrastructure teams to ensure seamless integration between model development and deployment, with a special focus on real-time and streaming audio inference.
Responsibilities
- Work on advancing core audio model serving metrics, including latency, throughput, and quality
- Dive deep into our systems, identify bottlenecks, and deliver creative solutions for audio processing and streaming workloads
- Collaborate closely with both the training and serving infrastructure teams to ensure seamless integration between model development and deployment, with a special focus on real-time and streaming audio inference
Qualifications
Minimum
- Significant experience developing high-performance audio or machine learning inference systems
- Proficiency with programming languages such as C++ and Python
- Hands-on experience with deep learning models for audio, speech, or language applications
- A bias for action and a strong results-oriented mindset
Preferred
- GPU programming, low-level system optimization, model parallelization techniques over multiple GPUs
- Experience with duplex real-time streaming architectures
- Internals of machine learning frameworks for audio (such as PyTorch, TensorFlow, or specialized audio libraries)
- Experience with inference framework like vLLM, SGLang, Tensort-LLM, or custom distributed inference systems
- Sequence modeling (e.g., transformers for audio/speech) and end-to-end audio pipeline optimization