
DataQueue empowers enterprises to build, test, and deploy intelligent AI voice agents with unparalleled performance. Whether you’re building a voice product or trying to handle millions of calls,…

DataQueue empowers enterprises to build, test, and deploy intelligent AI voice agents with unparalleled performance. Whether you’re building a voice product or trying to handle millions of calls,…
What they do: AI voice orchestration platform for building, testing, and deploying voice agents (phone and web/in-app)
Founded: June 1, 2022 (listed)
Headquarters: Washington, District of Columbia, United States
Employees: 58 (reported)
Recent funding: Seed round announced Jan 22, 2025
Enterprise conversational AI / voice agent orchestration for large-scale call volumes and complex workflows
2022
Artificial Intelligence / Voice Technology
Seed round announced Jan 22, 2025; reported investors include F6 Ventures and Ibtikar Fund
Reported Aug 2023 seed entry (Dealroom listing Ibtikar Fund)
2022 non-equity assistance entry reported in company record
“Ibtikar Fund and F6 Ventures listed as investors”
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DataQueue is the largest and fastest-growing Voice AI company in the MENA region, deploying AI voice agents across banks, telecoms, and governments — and scaling rapidly.
VoiceHub
, our platform, enables businesses to design, test, and deploy AI voice agents at scale, combining LLMs with a full voice stack including TTS, STT, copilots, speech analytics, and real-time workflows across 25+ languages.
We are looking for a Machine Learning Engineer
with strong expertise in Large Language Models (LLMs), Speech-to-Text (STT), and Text-to-Speech (TTS)
to help us build and optimize the next generation of voice AI systems. In this role, you will work on real-world production problems across model development, inference optimization, deployment, and scale.
You will be part of a team building intelligent conversational systems that must perform reliably under real enterprise requirements, with high standards for latency, accuracy, scalability, and natural interaction quality.
Responsibilities
Requirements
Strong Signal
We would be especially interested in candidates who have already:
Nice-to-Have Skills
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Model Development:
Research, develop, fine-tune, and improve LLM, STT, and TTS models for real-world conversational AI applications.
Training & Inference Pipelines:
Build and maintain efficient training, inference, and deployment pipelines for machine learning models in production environments.
Optimization:
Optimize models and pipelines for latency, accuracy, throughput, cost efficiency, and scalability.
Data Work:
Work with large-scale text and audio datasets, including preprocessing, augmentation, curation, and evaluation.
Production Integration:
Collaborate closely with software and platform engineers to integrate ML models into production-grade systems.
Inference Performance:
Improve real-time inference performance using modern frameworks and deployment techniques suited for large-scale AI systems.
Research & Experimentation:
Stay up to date with advancements in LLMs, speech technologies, deep learning, and model optimization, and apply them where relevant.
Engineering Standards:
Participate in code reviews and contribute to improving internal ML development workflows, tooling, and best practices.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field
3+ years of experience in machine learning and deep learning
Strong proficiency in Python
Strong hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX
Experience working with LLMs
such as GPT, BERT, or similar transformer-based models
Experience working with STT
models such as Whisper or similar
Experience working with TTS
models such as Tacotron, VITS, or similar
Strong understanding of NLP concepts including tokenization, embeddings, transformers, and sequence modeling
Experience building or optimizing training and inference pipelines
Familiarity with cloud-based ML environments such as AWS, GCP, or Azure
Experience optimizing models for low-latency or production inference
Strong understanding of data preprocessing, augmentation, and evaluation methodologies
Experience with vector databases
and retrieval-augmented generation (RAG)
Familiarity with high-performance inference frameworks such as vLLM
and TensorRT-LLM
Familiarity with model distillation
and quantization
techniques