Staff Machine Learning Engineer
About this role
Engineers who thrive on monitoring rapid advancements in machine learning and translating cutting-edge research into production-ready architectures will find a strong professional fit within this organization. Operating as part of NiCE Labs Research, the chosen candidate will directly shape the model expertise and agent framework powering the Cognigy platform.
Daily responsibilities center on evaluating and optimizing artificial intelligence models across agentic systems, spanning speech solutions like text-to-speech and speech-to-speech. The team focuses on identifying top-tier model candidates, building robust evaluation frameworks, and designing strategies that simultaneously enhance quality, reduce latency, and control operational costs.
What you will do
- Monitor the rapidly evolving artificial intelligence landscape to identify new state-of-the-art models and assess their applicability to specific Cognigy use cases.
- Design and execute comprehensive model evaluations, incorporating human-judged protocols for generated outputs alongside automated metric validation.
- Implement optimization strategies including fine-tuning, quantization, distillation, and efficient inference to enhance quality while lowering latency and cost.
- Deploy and benchmark various open-weight models across major cloud platforms to determine optimal hosting environments.
- Provide technical reviews and guidance regarding model optimization initiatives undertaken by teammates.
- Communicate research results, benchmarks, and strategic recommendations clearly to both technical and non-technical stakeholders.
What you need
- Hold a Master of Science degree in computer science, machine learning, data science, or a related technical discipline.
- Bring a minimum of three years of post-graduate, hands-on professional experience encompassing machine learning model training, fine-tuning, and evaluation.
- Demonstrate practical background with advanced model optimization techniques such as quantization, distillation, or efficient inference methods.
- Possess proven experience designing evaluation frameworks or benchmarks for artificial intelligence systems, including subjective and human-rated measures.
- Show strong programming proficiency utilizing Python alongside either PyTorch or TensorFlow frameworks.
- Have working familiarity with cloud machine learning infrastructure across AWS, Azure, or GCP for model testing and deployment purposes.
Nice to have
- 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
- Docker, microservice deployment, and GPU inference serving
Benefits
- NiCE-FLEX hybrid work model offering maximum flexibility with 2 days in the office and 3 days remote each week
- Endless internal career opportunities across multiple roles, disciplines, domains, and locations
Skills and tech stack
Questions candidates ask
What is the remote work policy for this job?
The team operates under the NiCE-FLEX hybrid model, which requires working from the office two days a week and allows three days of remote work. Office days are specifically designated for face-to-face meetings, teamwork, and collaborative thinking.
What team will I be joining?
You will join NiCE Labs Research, which is the specific group responsible for model expertise and agent architecture for the Cognigy platform. The role reports directly to the Director of Engineering for AI Research.
What kind of systems and models will I work with?
The position involves evaluating and optimizing AI models across agentic systems, including text-to-speech and speech-to-speech speech models. Responsibilities also include tracking the model landscape and deploying open-weight models on cloud platforms.