Associate Machine Learning Engineer, Enable

• We are seeking a detail-oriented engineer passionate about the foundational elements of machine learning
• The ideal candidate is a junior engineer eager to learn how clean data and seamless operations power impactful ML solutions across the business
• Foundational knowledge of Python (NumPy, pandas) and basic SQL
• Familiarity with data preprocessing and validation techniques, with an interest in synthetic data tools such as Synthea or Faker
• Curiosity and developing skills in MLOps tools such as Kubeflow or TFX and cloud environments (AWS, GCP, Azure)
• Strong analytical and problem-solving abilities with attention to data integrity and systematic execution
• Effective communication skills for cross-functional collaboration across technical and non-technical teams
• Bachelor’s degree in Computer Science, Data Science, or a related technical field, or equivalent practical experience
• Foundational experience working with data pipelines, model development, or ML workflows through coursework or early professional projects

Desirable

• This individual will play a key role in building robust data pipelines, improving dataset quality, and streamlining model deployment processes
• Support data quality assurance by cleaning, validating, and auditing datasets to ensure accuracy and completeness
• Identify and resolve data anomalies, biases, and missing values in partnership with data engineering teams
• Assist in the development of automated validation tools using Python (pandas, Great Expectations)
• Contribute to synthetic data creation efforts by helping design synthetic datasets to augment training data or simulate edge cases
• Learn techniques such as GANs, VAEs, and rule-based simulation under mentorship
• Test and evaluate synthetic data efficacy in model training pipelines
• Assist with automated labeling by supporting labeling pipelines using tools such as Snorkel, weak supervision, or active learning
• Collaborate with annotation teams to improve labeling workflows and reduce manual intervention
• Provide MLOps support in deploying and monitoring models in production environments (Azure) using tools such as Docker, Airflow, or MLflow
• Assist in building CI/CD pipelines for automated model testing and retraining
• Help document processes and maintain performance dashboards