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ML Engineer, Voice & AI
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The role
Evaluate and fine-tune open-source speech recognition models to enable new capabilities and improve quality.
Optimize inference infrastructure (WhisperX, Kubernetes, NATS) for stability and performance in production.
Collaborate with a small, fast-moving team to explore Open Source large language models and apply them to real-world voice AI features.
Translate customer and clinician feedback into measurable ML improvements with clear business impact.
Develop data preparation pipelines and synthetic data generation to boost model robustness and coverage.
Own end-to-end ML work from experimentation to deployment in a hybrid, mission-driven environment with regular in-person collaboration three days per week.
Optimize inference infrastructure (WhisperX, Kubernetes, NATS) for stability and performance in production.
Collaborate with a small, fast-moving team to explore Open Source large language models and apply them to real-world voice AI features.
Translate customer and clinician feedback into measurable ML improvements with clear business impact.
Develop data preparation pipelines and synthetic data generation to boost model robustness and coverage.
Own end-to-end ML work from experimentation to deployment in a hybrid, mission-driven environment with regular in-person collaboration three days per week.
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