10 дней назад
Principal Engineer - Machine Learning (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Principal Engineer - Machine Learning (AI): Building and operating machine learning systems for product development, including deep learning inspection models, real-time anomaly detection, surrogate modeling, and active learning pipelines with an accent on computer vision, uncertainty analysis, and MLOps reliability. Focus on owning model performance end to end, defining data-to-model interfaces, calibrating experiments and thresholds, and maintaining production-ready training and deployment workflows.
Location: Singapore office
Company
develops storage systems and infrastructure for AI-driven data centers, cloud platforms, and enterprise environments.
What you will do
- Build, train, evaluate, and maintain CNN, U-Net, and ViT models for automated inspection and measurement.
- Own model performance through ablation studies, confidence calibration, monitoring, and degradation escalation.
- Develop real-time anomaly detection for product-development sensor and time-series data.
- Implement surrogate-model pipelines and active-learning systems, including acquisition functions and versioned feature sets.
- Define the data contract between data engineering and ML systems, validating datasets before training.
- Maintain model versions, training pipelines, containers, MLflow tracking, CI/CD contributions, documentation, and production handoff.
Requirements
- Bachelor’s or Master’s degree in AI, Machine Learning, Computer Science, or a related field.
- 1–3 years of hands-on ML engineering experience, or equivalent depth through internships, research, or open-source contributions.
- Strong Python skills and proficient to expert-level PyTorch experience.
- Strong computer vision fundamentals with practical depth in CNNs and at least one of U-Net or segmentation, ViT or transformer-based vision, or time-series anomaly detection.
- Experience with surrogate modeling, active learning, data validation, model evaluation, uncertainty analysis, and technical documentation.
- Practical MLOps experience with MLflow, Docker, Git, and basic CI/CD contributions.
Nice to have
- PINNs, Bayesian methods, Bayesian neural networks, Gaussian processes, ensemble uncertainty, or calibration experience.
- Reinforcement learning fundamentals, including gym environments and policy-gradient concepts.
- AWS fundamentals such as S3, EC2, and SageMaker, or RAG pipeline experience.
Culture & Benefits
- Work on storage infrastructure supporting AI-driven data systems at global scale.
- Contribute to an inclusive environment focused on diversity, respect, and belonging.
- Accessibility support is available throughout the application and hiring process.
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