Machine Learning Engineer

At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.

So what is the role about?

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 (text-to-speech and speech-to-speech). You will track the model landscape, identify state-of-the-art candidates, and develop strategies to improve quality and latency while reducing cost. You will work closely with NLR colleagues to extend the team's evaluation framework and build proof-of-concept implementations that demonstrate your recommendations.

How will you make an impact?

• 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.

Have you got what it takes?

• 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.

You will have an advantage if you have: