ML Engineer

ML engineer - Maps Data Systems Description In this role, you will design and build machine learning systems and perform data analysis to improve the quality of large-scale geospatial datasets. You will develop both classical machine learning and NLP models to extract structured information, detect anomalies, and measure data quality. You will be involved end-to-end—from experimentation and model development to integration, productionization, and deployment at scale. This role requires a strong combination of data science expertise and solid software engineering skills. Responsibilities Design, develop, and deploy machine learning models Build and maintain scalable data and software solutions Apply data science techniques to generate insights and improve data quality Own and deliver features and improvements end-to-end Collaborate with cross-functional teams to integrate models into production systems Must-have Qualifications Strong programming skills, with extensive experience in Python Proven experience in machine learning and data science (e.g., classification, clustering, feature engineering, anomaly detection, neural networks) Experience working with LLMs, including prompting and agent-based workflows Solid understanding of classical ML algorithms (e.g., SVM, Random Forest, Naive Bayes, k-NN) Experience with ML frameworks and libraries such as PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas Strong analytical thinking and problem-solving skills Ability to balance creativity with practical delivery in existing systems Excellent written and verbal communication skills Proactive, ownership-driven mindset with the ability to adapt and deliver in a fast-paced environment Extra Merit Qualifications Experience with Scala or Java Knowledge in one or more of the following areas: NLP, information retrieval, data mining Experience in statistical modeling and building predictive models

Last date to apply: 2026-06-20