NielsenIQ is looking for a Machine Learning Engineer to build, train, and optimize machine learning models that solve business problems. In this role, you will support the implementation of Generative AI use cases and collaborate closely with senior engineers and stakeholders within an Agile development environment.
What You'll Do
- Build, train, and optimize machine learning models to solve business problems.
- Support implementation of Generative AI use cases using frameworks like LangChain, Hugging Face, or similar.
- Clean, preprocess, and transform data for model development and deployment.
- Run experiments, evaluate results, and fine-tune models for performance and scalability.
- Work closely with senior engineers and stakeholders to understand requirements and deliver solutions in Agile sprints.
- Maintain structured documentation of code, experiments, and model performance for reproducibility.
- Stay updated with new tools, frameworks, and best practices in ML/AI and apply them in projects.
What We're Looking For
- Proficiency in Python.
- Experience in building ML models using Scikit-learn, PyTorch, or TensorFlow.
- Familiarity with NLP libraries (e.g., Hugging Face, NLTK, SpaCy, Spark NLP).
- Hands-on experience with version control (GitHub) and basic CI/CD pipelines.
- Strong understanding of data structures, algorithms, and model evaluation techniques.
Nice to Have
- Exposure to Generative AI frameworks (LangChain, AutoGen, etc.).
- Experience with MLOps tools (MLflow, AutoML, etc.).
- Familiarity with Azure Cloud or other cloud environments.
- Experience working in an Agile model.
Technical Stack
- Languages & Libraries: Python, Scikit-learn, PyTorch, TensorFlow
- NLP: Hugging Face, NLTK, SpaCy, Spark NLP
- Tools & Platforms: GitHub, CI/CD pipelines
- Generative AI: LangChain, AutoGen
- MLOps: MLflow, AutoML
- Cloud: Azure Cloud
Benefits & Compensation
- Flexible working environment
- Volunteer time off
- LinkedIn Learning
- Employee-Assistance-Program (EAP)
All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws.
