Machine Learning Model Optimization Developer Jobs

1 job found

2d ago

Staff Machine Learning Engineer

Nice Sandy, UT

Sandy, UT

2d ago

Full time Artificial Intelligence Cloud Machine Learning Data Science

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Job Description

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.

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