Staff Machine Learning Engineer

NiCE Labs Research (NLR) is seeking a Staff Machine Learning Engineer to help evaluate and optimize AI models powering Cognigy’s agentic systems, including speech models. In this individual contributor role, you will keep pace with state-of-the-art options, run rigorous evaluations, and drive improvements across quality, latency, and cost through practical optimization and deployment work.

What you will do

• Track emerging machine learning advancements and assess how new state-of-the-art models and optimization techniques apply to Cognigy use cases, with a focus on ML, model optimization, and agentic AI.
• Design and run model evaluations, including human-judged protocols for generated outputs and validation of automated metrics against human ratings.
• Plan and execute optimization approaches such as fine-tuning, quantization, distillation, and efficient inference to improve quality while reducing latency and cost.
• Deploy and benchmark open-weight models on cloud platforms and compare options for hosting.
• Provide technical review and guidance to teammates working on model optimization.
• Share evaluation outcomes and recommendations with both technical and non-technical stakeholders.

What you bring

• Education: MS in computer science, machine learning, data science, or a related field.
• Experience: 3+ years of post-graduate, hands-on experience with ML models, including training, fine-tuning, and evaluation.
• Experience with model optimization techniques such as quantization, distillation, or efficient inference.
• Experience designing evaluations or benchmarks for AI systems, including subjective or human-rated measures.
• Proficiency in Python and PyTorch or TensorFlow.
• Experience with cloud ML infrastructure such as AWS, Azure, or GCP for model testing and deployment.
• Ability to collaborate across cross-functional teams, stay effective in a fast-changing environment with shifting priorities, and present clearly to internal and external stakeholders.

Technologies

• Python
• PyTorch
• TensorFlow
• AWS
• Azure
• GCP

Advantage areas

• Experience evaluating or fine-tuning TTS or S2S models for production use, or related audio and speech work.
• Exposure to agentic AI frameworks or conversational AI platforms.
• Experience with Docker, microservice deployment, and GPU inference serving.

Role details

• Location: Sandy, UT (hybrid)
• Reporting into: Director, Engineering, AI Research, NiCE Labs
• Role type: Individual Contributor

Benefits

• NiCE-FLEX hybrid model: 2 days working from the office and 3 days of remote work each week.