Company Description
kuubiik is a global consulting company headquartered in Singapore, offering outsourcing and project-based solutions across all business functions in over 150 countries. With a diverse team spanning Asia, Europe, and the Americas, kuubiik has earned the trust of renowned brands like Google, TikTok, HP, and TELUS. Focused on flexibility, kuubiik offers hourly or full-time outsourcing services tailored to meet client needs. The company emphasizes collaboration and innovation, delivering customized solutions at competitive rates.
Role Overview:
- Employment: Full-Time
- Type: Permanent
- Working Hours: Monday - Friday l 9:00 AM - 5:00 PM SGT
- Location: Onsite, Singapore
Core Responsibilities
1. Predictive Model Development & Research
- Design, build, and optimize machine learning and deep learning models for anomaly detection, failure prediction, and fault diagnosis.
- Analyze complex, noisy, high-dimensional IoT time-series sensor streams (vibration, acoustic, thermal, electrical).
- Benchmark state-of-the-art research from academic literature and translate novel condition-monitoring techniques into production-ready algorithms.
- Design rigorous experiments to validate hypotheses against real-world machine failure datasets.
2. Production MLOps & System Architecture
- Deploy scalable, low-latency AI models into cloud environments (AWS, GCP, or Azure) using production-grade MLOps practices.
- Build real-time monitoring tools to track model drift, latency, and performance in live industrial settings.
- Continuously retrain and refine algorithms based on real-world feedback, test results, and edge edge-case data.
3. Cross-Functional Collaboration & Field Execution
- Work alongside engineering teams to optimize raw data pipelines and refine input features.
- Document system architectures, model performance metrics, and actionable insights for internal and external executive stakeholders.
- IoT Field Engineering Support: Assist in planning, installing, and configuring IoT hardware and software solutions at client sites when needed to ensure uninterrupted data flow and field stability.
Requirements & Qualifications
- Core Expertise: Proven track record in machine learning, complex time-series analysis, signal processing, and anomaly/failure detection.
- Programming Languages: Proficiency in Python (R or Java familiarity is a plus).
- ML Frameworks: Hands-on experience with TensorFlow, PyTorch, and Scikit-learn.
- Cloud & MLOps: Practical experience deploying AI models on cloud infrastructure (AWS, GCP, or Azure).
- Domain Data Knowledge: Strong background working with physical sensor data (e.g., Fourier transforms, vibration analysis, current signatures, pressure/temperature metrics).
- Exceptional problem-solving skills with a strong ability to handle noisy, incomplete, or high-dimensional datasets.
- Strong communication skills to bridge technical data science with operational client needs.
- High degree of curiosity, self-drive, and comfort working in high-velocity startup environments.
- 2+ years of experience in industrial, manufacturing, or heavy-machinery settings.
- Deep understanding of both data-driven and physics/model-based condition monitoring.
- Experience developing agentic AI systems, root-cause analysis engines, or prescriptive maintenance frameworks.