[Remote] Audio Engineer, Model Efficiency at Cohere

New York, New York, United States

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Not SpecifiedCompensation

N/AExperience Level

N/AJob Type

Not SpecifiedVisa

N/AIndustries

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 a strong results-oriented mindset
GPU programming experience
Low-level system optimization experience
Model parallelization techniques experience 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, Tensort-LLM, or custom distributed inference systems
Sequence modeling (e.g., transformers for audio/speech) and end-to-end audio pipeline optimization

Responsibilities

Advancing core audio model serving metrics, including latency, throughput, and quality
Diving deep into systems to identify bottlenecks
Delivering creative solutions for audio processing and streaming workloads
Collaborating with training and serving infrastructure teams to ensure seamless integration between model development and deployment
Focusing on real-time and streaming audio inference

Skills

Cohere

Provides NLP tools and LLMs via API

About Cohere

Cohere provides advanced Natural Language Processing (NLP) tools and Large Language Models (LLMs) through a user-friendly API. Their services cater to a wide range of clients, including businesses that want to improve their content generation, summarization, and search functions. Cohere's business model focuses on offering scalable and affordable generative AI tools, generating revenue by granting API access to pre-trained models that can handle tasks like text classification, sentiment analysis, and semantic search in multiple languages. The platform is customizable, enabling businesses to create smarter and faster solutions. With multilingual support, Cohere effectively addresses language barriers, making it suitable for international use.

Toronto, CanadaHeadquarters

2019Year Founded

$914.4MTotal Funding

SERIES_DCompany Stage

AI & Machine LearningIndustries

501-1,000Employees

Risks

Competitors like Google and Microsoft may overshadow Cohere with seamless enterprise system integration.

Reliance on Nvidia chips poses risks if supply chain issues arise or strategic focus shifts.

High cost of AI data center could strain financial resources if government funding is delayed.

Differentiation

Cohere's North platform outperforms Microsoft Copilot and Google Vertex AI in enterprise functions.

Rerank 3.5 model processes queries in over 100 languages, enhancing multilingual search capabilities.

Command R7B model excels in RAG, math, and coding, outperforming competitors like Google's Gemma.

Upsides

Cohere's AI data center project positions it as a key player in Canadian AI.

North platform offers secure AI deployment for regulated industries, enhancing privacy-focused enterprise solutions.

Cohere's multilingual support breaks language barriers, expanding its global market reach.

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