Machine Learning Model Optimization Data Jobs

1 job found

Today

Staff Machine Learning Engineer

Nice Sandy, UT

Sandy, UT

Today

Full time Artificial Intelligence Cloud Machine Learning Data Science

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Job Description

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

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