Machine Learning jobs in Singapore

We are looking for a talented and motivated Machine Learning Engineer with a strong foundation in classical machine learning techniques to join our innovative team in the manufacturing and semiconductor sectors. This role focuses on developing robust, data-driven solutions to detect anomalies, predict maintenance needs, and monitor market dynamics, ensuring our clients maintain operational excellence and competitive edge.

As part of our analytics engineering group, you will tackle real-world problems using numerical datasets from sensors and tests, while also applying NLP methods to extract insights from unstructured data sources. This position is ideal for an early-career professional eager to contribute to high-impact projects in a collaborative, fast-paced environment, where precision and reliability are paramount.

• Design and implement anomaly detection systems to monitor sensor and test data in real-time, identifying deviations that could indicate equipment issues or process inefficiencies in semiconductor manufacturing environments.
• Develop predictive maintenance models using classical machine learning algorithms, leveraging numerical datasets to forecast potential failures and provide actionable recommendations to customers, thereby minimizing downtime and optimizing resource allocation.
• Build and maintain monitoring pipelines for competitor pricing data scraped from online sources, incorporating drift detection mechanisms to ensure model reliability over time and alert on significant market shifts.
• Tackle a variety of classical ML challenges, including regression, classification, and clustering tasks, utilizing libraries such as XGBoost and Scikit-learn to deliver scalable, interpretable solutions.
• Address NLP-related problems by applying innovative approaches, including classical recurrent architectures like RNNs or LSTMs, to process and analyze textual data (e.g., from market reports or operational logs) alongside traditional ML workflows.
• Collaborate with data engineers, domain experts, and product teams in an Agile framework to iterate on models, conduct thorough validation, and deploy solutions into production.
• Perform rigorous model evaluation, hyperparameter tuning, and feature engineering to enhance accuracy and generalizability, with a focus on handling imbalanced datasets and ensuring ethical AI practices.
• Contribute to data pipeline improvements, potentially using tools like Apache Spark for large-scale processing, and stay current with evolving ML methodologies to propose enhancements.

• Bachelor's or Master's degree in Machine Learning, Computer Science, Data Science, Statistics, or a related quantitative field.
• 2–4 years of hands-on experience in classical machine learning (including deep learning) or data science roles, with demonstrated ability to build, train, and validate models from scratch.
• Proficiency in classical ML frameworks and libraries, including Scikit-learn for core algorithms, XGBoost for gradient boosting, and experience with NLP techniques using RNNs/LSTMs (e.g., via TensorFlow or PyTorch).
• Strong programming skills in Python, with a focus on clean, maintainable code, version control (Git), and basic data manipulation using Pandas and NumPy.
• Familiarity with anomaly detection, predictive modeling, and concept drift monitoring in numerical datasets.
• Experience working in Agile/Scrum environments, including sprint-based development and cross-functional collaboration.
• Solid understanding of model evaluation metrics, cross-validation, and QA processes for ML systems.

• Exposure to industrial domains such as manufacturing, semiconductors, or sensor-based analytics.
• Knowledge of big data tools like Apache Spark for distributed processing and handling large-scale datasets.
• Prior work on NLP tasks integrated with classical ML, such as sentiment analysis or entity recognition in unstructured data.
• A portfolio of projects showcasing innovative solutions to real-world ML problems.