Senior Applied Machine Learning Scientist

Sanas

full-time

Posted on: 9/21/2025

Origin: • 🇺🇸 United States

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Job Level

Senior

Tech Stack

AWSAzureCloudGoogle Cloud PlatformPyTorchTensorflow

About the role

Architect, train, and optimize large-scale speech AI models, including speech-to-speech, speech restoration, and speech translation.
Leverage self-supervised learning, contrastive learning, and transformer-based architectures (e.g., wav2vec, Whisper, GPT-style models) to improve model accuracy and adaptability.
Develop efficient model distillation and quantization strategies to deploy large models with low-latency inference.
Innovate on cross-lingual and multilingual speech processing using large-scale pretraining and fine-tuning.
Curate and scale massive diverse, multilingual, and multimodal datasets for robust model training.
Apply active learning, domain adaptation, and synthetic data generation to overcome data limitations.
Lead efforts in data quality assessment, augmentation, and curation for large-scale training pipelines.
Develop distributed training strategies for large-scale models using cloud-based and on-prem GPU clusters.
Design and implement scalable model evaluation frameworks, tracking WER, MOS, and latency across diverse scenarios.
Optimize real-time inference pipelines to ensure high-throughput, low-latency speech processing.
Collaborate with academia, open-source communities, and research partners to drive innovation.
Work closely with MLOps, Data Engineering, and Product teams to deploy scalable AI systems.
Ensure seamless integration of foundational models with edge devices, real-time applications, and cloud platforms.
Translate cutting-edge research into production-grade models that power real-world communication.

Requirements

Bachelor’s, Master’s or Ph.D. in Computer Science, Electrical Engineering, or a related field with a focus on Machine Learning, Deep Learning, or Speech Processing.
5+ years of hands-on industry experience in developing and implementing the following systems: Speech-to-text (ASR), Text-to-speech (TTS), Voice conversion & speech enhancement, Speech translation & multimodal learning.
Strong proficiency in transformer-based architectures (e.g., wav2vec 2.0, Whisper, GPT, BERT).
Expertise in deep learning frameworks such as PyTorch, TensorFlow, and large-scale training techniques.
Experience with distributed training and optimization across multi-GPU clusters.
Strong understanding of self-supervised learning, contrastive learning, and generative modeling for speech AI.
Hands-on experience with cloud-based AI platforms (AWS, GCP, Azure) and model deployment.
Experience curating and scaling massive diverse, multilingual, and multimodal datasets for robust model training.
Experience with active learning, domain adaptation, and synthetic data generation.
Experience with data quality assessment, augmentation, and curation for large-scale training pipelines.
Experience developing distributed training strategies for large-scale models using cloud-based and on-prem GPU clusters.
Experience designing and implementing scalable model evaluation frameworks and tracking WER, MOS, and latency.
Experience optimizing real-time inference pipelines for high-throughput, low-latency speech processing.
Preferred: Experience in developing multimodal AI models integrating speech, text, and vision.
Preferred: Track record of publishing in top-tier AI/ML conferences.
Preferred: Experience optimizing large models for real-time inference on edge devices.
Preferred: Proficiency with MLOps best practices for deploying and monitoring models in production.
Preferred: Familiarity with open-source ASR/TTS toolkits.

Senior Applied Machine Learning Scientist

Job Level

Tech Stack

About the role

Requirements

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