Audio Inference Engineer, Model Efficiency

Who are we?

Role Overview

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.

Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations.

Qualifications

• 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.

It is a big plus if you also have considerable experience with

• GPU programming, low-level system optimization, model parallelization techniques over multiple GPUs
• Have experience with duplex real-time streaming architectures.
• Internals of machine learning frameworks for audio (such as PyTorch, TensorFlow, or specialized audio libraries).
• Have 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

Working location

This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.