Staff Machine Learning Engineer

USA - Sandy, UT · 3+ years experience · Hybrid · Posted Sep 30, 2026

At a glance

NICE Ltd. (NASDAQ: NICE) software products are used by 25,000+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3+ billion financial transactions.
Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30+ countries.
NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.

NiCE is looking for a Staff Machine Learning Engineer to join NiCE Labs Research (NLR), the team responsible for model expertise and agent architecture for the Cognigy platform.
You will evaluate and optimize AI models across Cognigy's agentic systems, including speech models.
You will track the model landscape, identify state-of-the-art candidates, and develop strategies to improve quality and latency while reducing cost.

Monitor the field for new state-of-the-art models and assess their 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 output and validation of automated metrics against human ratings.
Design and execute optimization strategies (fine-tuning, quantization, distillation, efficient inference) to improve quality, reduce latency, and lower cost.
Deploy and benchmark open-weight models on cloud platforms and compare platforms for hosting.
Provide technical review and guidance on teammates' model optimization work.
Communicate results and recommendations to technical and non-technical stakeholders.

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 (AWS, Azure, or GCP) for model testing and deployment.
Ability to build working relationships with cross-functional teams, keep pace with a fast-changing field and shifting priorities, and present clearly to internal and external stakeholders.

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