Senior Machine Learning Engineer

NICE•Sandy, UT

29d•Hybrid

About The Position

NiCE is looking for a Senior Machine Learning Engineer to join NiCE Labs Research (NLR), a team dedicated to model expertise and agent architecture for the Cognigy platform. As Senior Machine Learning Engineer, you will own the evaluation and optimization of speech-oriented AI models — covering real-time transcription and speech-to-speech systems across dozens of languages. This role is primarily concerned with rigorous measurement: designing test suites, running comparative evaluations, and producing actionable recommendations on model selection and configuration. The Senior Machine Learning Engineer monitors the rapidly evolving speech AI landscape to identify state-of-the-art transcription and speech-to-speech models for evaluation. You will design and maintain a speech-oriented test suite that covers quality, cost, and latency, and develop techniques to optimize model usage for operational deployment. This role requires deep expertise in speech AI systems, strong quantitative skills, and the discipline to produce reliable, reproducible evaluation results.

Requirements

MS in computer science, electrical engineering, computational linguistics, or a related field with a focus on speech or audio processing.
Three or more years of hands-on experience with speech AI systems, including ASR, TTS, or speech-to-speech models.
Experience designing evaluation methodologies or test suites for AI systems.
Strong quantitative and analytical skills, with experience producing rigorous benchmark results.
LoRA/PEFT for speech models, inference optimization (quantization, SGLang/vLLM serving for audio, distillation), experience with at least one open-source TTS family
GPU cost modeling
Proficiency in Python and familiarity with speech processing libraries and tools.
Experience with cloud-based infrastructure (AWS, Azure, or GCP).
Ability to develop and maintain good working relationships with cross-functional teams.
Ability to clearly communicate and present to internal and external stakeholders.

Nice To Haves

Experience evaluating speech models across multiple languages.
Familiarity with multi-cloud deployment across AWS, Azure, and Google Cloud.
Experience with model optimization techniques for speech systems, such as latency reduction or cost optimization.
Exposure to contact center or conversational AI platforms.
Experience working on international, globe-spanning teams.

Responsibilities

Design and maintain a speech-oriented test suite covering quality, cost, and latency across dozens of languages.
Monitor the industry for new state-of-the-art transcription and speech-to-speech models to evaluate.
Design and evaluate techniques to optimize speech model usage for operational deployment.
Produce clear, quantitative evaluation reports and model recommendations for technical and non-technical stakeholders.
Contribute to the broader model evaluation framework maintained by the NLR team.
Stay informed of advances in speech AI, including transcription, text-to-speech, and speech-to-speech technologies.