5 дней назад
Staff 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
Staff Engineer - Machine Learning (AI/Deep Learning): Building and evaluating CNN-based models and ML pipeline components for precision product development with an accent on training data quality, experiment tracking, and model evaluation. Focus on validating scientific datasets, packaging models with Docker, contributing to CI/CD, and supporting inference and deployment validation.
Location: Singapore office
Company
builds large-scale storage systems and infrastructure for AI-driven data platforms, hyperscale data centers, cloud platforms, and enterprise environments.
What you will do
- Track assigned machine learning experiments in MLflow and produce structured evaluation reports.
- Validate training data through feature distribution checks, label verification, and anomaly detection.
- Build and train CNN-based models for image classification and defect detection.
- Compare model configurations, analyze training runs, and contribute to evaluation cycles.
- Package Python and machine learning models with Docker and contribute to CI/CD scripts.
- Run inference tests and support deployment validation in product development environments.
Requirements
- Bachelor's or Master's degree in AI, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
- Fresh graduate to one year of experience; strong machine learning project or thesis experience is required, while professional experience is not necessary.
- Strong Python proficiency, including clean ML code and NumPy/Pandas fundamentals.
- Foundational PyTorch skills, CNN architecture knowledge, model evaluation, MLflow, and Docker.
- Conceptual understanding of surrogate modeling, active learning, and uncertainty-guided data selection.
- Ability to learn independently and quickly apply new technical concepts.
Nice to have
- Exposure to U-Net, ViT, uncertainty quantification, Bayesian methods, time-series or sensor data, reinforcement learning, RAG, or LLM projects.
- AWS fundamentals or entry-level cloud machine learning deployment experience.
- Research internship experience.
Culture & Benefits
- Work with engineers and researchers on scientific machine learning problems in precision product development.
- Receive direct mentorship from experienced engineers and researchers.
- Contribute to real production-oriented workflows rather than toy projects.
- Join an inclusive environment focused on diversity, belonging, respect, and contribution.
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