Staff Machine Learning Engineer

NiCE (NiCE Labs Research) is hiring a Staff Machine Learning Engineer to evaluate, optimize, and deploy AI models across agentic systems, including speech models.

Responsibilities

• Track new state-of-the-art ML models and assess relevance to Cognigy use cases
• Stay current on advances in ML, model optimization, and agentic AI
• Design and run model evaluations, including human-judged protocols for generated outputs
• Validate automated metrics against human ratings
• Develop and execute optimization strategies to improve quality, reduce latency, and lower cost, including fine-tuning, quantization, distillation, and efficient inference
• Deploy and benchmark open-weight models on cloud platforms and compare hosting options
• Provide technical review and guidance on teammates’ model optimization work
• Communicate evaluation results and recommendations to technical and non-technical stakeholders

Requirements

• MS in computer science, machine learning, data science, or a related field
• 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 for model testing and deployment: AWS, Azure, or GCP
• Ability to collaborate cross-functionally, adapt to a fast-changing field and shifting priorities, and present clearly to internal and external stakeholders

Technologies

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

Preferred (Advantage)

• 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

Location and Work Style

• Sandy, UT (hybrid)

Reporting and Role Type

• Reports to: Director, Engineering, AI Research, NiCE Labs
• Role type: Individual Contributor

Benefits

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