Overview As a Senior Applied Scientist at Qualtrics, you will advance ML/AI research to personalize the platform and create a competitive edge through AI features. You'll partner with cross-functional teams to translate business needs into scalable algorithms and models, covering training, evaluation, explainability, and deployment. You'll tackle challenging problems with generative and agentic systems, shaping the user experience across diverse industries. This role offers impact through research-driven products and collaboration at a global scale.
Compensation / Benefits- Wellness reimbursement
- Experience bonus
- Hybrid work model
- Employee communities (QGroup MOSAIQ, Green Team, Qualtrics Pride, Q Able)
- Disability accommodations provided
Responsibilities- Research, implement, evaluate, optimize, and productionize cutting-edge ML models to support business growth
- Stay current with ML/AI advances and present findings to the team and broader community
- Collaborate with engineers, product managers, and other specialists to gather requirements for AI applications
- Lead design reviews, modeling discussions, and define technical requirements
- Mentor junior scientists and promote best practices for experimentation, reproducibility, and lifecycle management
- Champion Evaluation-Driven Development by embedding automated testing, risk assessments, and production monitoring into the lifecycle of agentic systems
Key requirements- 2+ years of post-graduate industrial research experience in ML/NLP/info retrieval/deep learning or related field
- Deep learning implementation expertise (MXNet, TensorFlow, PyTorch, etc)
- Excellent communication, writing and presentation skills
- Proficiency in Python and modern DL frameworks
- Strong problem solving and ML lifecycle management understanding
- Experience building production-quality, large-scale ML deployments
- Expertise in NLP, information retrieval, speech processing, deep learning, or reinforcement learning
- Experience with ML systems/tools (SageMaker, MLFlow) and benchmarking in CI/CD pipelines
- Publications in top-tier ML/NLP conferences preferred
- Excellent communication and presentation
- Collaborative mindset
- Mentoring and leadership
- Natural Language Processing
- Reinforcement Learning
- Supervised and unsupervised learning
