Staff Machine Learning Engineer

The Staff Machine Learning Engineer role at NiCE Labs Research (NLR) focuses on evaluating and optimizing AI models that support Cognigy’s agentic systems, including speech models. The position blends hands-on ML evaluation with optimization, benchmarking, and collaboration across technical and non-technical stakeholders in a fast-moving research environment.

How You Will Contribute

• Track developments in machine learning and agentic AI, monitor state-of-the-art model releases, and assess their relevance to Cognigy use cases.
• Design and run model evaluations, including human-judged protocols for generated outputs and validation of automated metrics against human ratings.
• Plan and execute model 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 hosting options to support performance and cost goals.
• Review and provide technical guidance on teammates’ model optimization work.
• Communicate evaluation outcomes, insights, and recommendations to internal technical and non-technical stakeholders.

What You Will Need

• Education: MS in computer science, machine learning, data science, or a related field.
• Experience: 3+ years of post-graduate, hands-on experience working with ML models, including training, fine-tuning, and evaluation.
• Hands-on experience with model optimization methods such as quantization, distillation, or efficient inference.
• Experience designing evaluations or benchmarks for AI systems, including subjective or human-rated measurement approaches.
• Proficiency in Python and PyTorch or TensorFlow.
• Experience using cloud ML infrastructure for testing and deployment, including AWS, Azure, or GCP.
• Ability to build effective cross-functional working relationships, adapt to shifting priorities, and present results clearly to stakeholders.

Tools and Technologies

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

Preferred Qualifications

• 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.
• Familiarity with Docker, microservice deployment, and GPU inference serving.

Role Details

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

Benefits

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