Develops high-performance audio inference systems, optimizing latency, throughput, and quality for real-time streaming workloads. Requires expertise in C++, Python, and deep learning models for audio/speech, with collaboration across training and serving teams.
Salary not listed
RemoteML Engineering
About the role
Responsibilities
Advance core audio model serving metrics, including latency, throughput, and quality.
Dive deep into systems, identify bottlenecks, and deliver creative solutions for audio processing and streaming workloads.
Collaborate closely with training and serving infrastructure teams for seamless integration between model development and deployment.
Special focus on real-time and streaming audio inference.
Requirements
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.
Bias for action and strong results-oriented mindset.
Nice-to-Haves
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 (PyTorch, TensorFlow, or specialized audio libraries).
Experience with inference frameworks like vLLM, SGLang, TensorRT-LLM, or custom distributed inference systems.
Sequence modeling (e.g., transformers for audio/speech) and end-to-end audio pipeline optimization.
Characterize, analyze, and optimize performance of state-of-the-art AI models on Cerebras' wafer-scale hardware. Build performance models, optimize kernels and compilers, debug runtime behavior, and develop visualization tools to influence next-gen AI architecture.
Salary not listed
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