Machine Learning Engineer

5+ years of experience in machine learning and NLP.
Experience working in a Linux environment.
Strong proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
Hands-on experience with SpaCy, Hugging Face, and Transformers for NLP applications.
Expertise in working with sparse data and applying techniques such as data augmentation, weak supervision, and semi-supervised learning.
Experience deploying and managing ML models in cloud-based environments (e.g. AWS SageMaker).
Strong understanding of MLOps principles, including automated model retraining, performance monitoring, and infrastructure scaling.
Experience with data evaluation techniques, model explainability, and error analysis.
Solid grasp of NLP concepts, including tokenization, embeddings, attention mechanisms, and transformer-based architectures.
Experience fine-tuning large-scale NLP models and LLMs.
Familiarity with knowledge graphs and graph-based NLP techniques.
Background in unsupervised learning or self-supervised learning for NLP.
Commitment to the highest ethical standards.

Contribute to projects across various ML disciplines, including Natural Language Processing (NLP), unstructured data analysis, predictive modeling, and classic machine learning.
Work with sparse data and apply techniques to improve model accuracy and generalization.
Utilize SpaCy, Hugging Face Transformers, PyTorch, TensorFlow, and other NLP frameworks for model development.
Implement MLOps strategies, including model versioning, automated retraining, monitoring, and CI/CD pipelines for ML workflows.
Conduct data evaluation, including data preprocessing, feature engineering, and model performance assessment.
Collaborate cross-functionally with data engineers, software developers, and product teams to integrate models into production systems.
Stay up to date with the latest advancements in NLP and machine learning, applying new techniques as needed.

No preferred qualifications provided.