Machine Learning Engineer

• 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.

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