Job Requirements
Job benefits
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Remote work options
Thanks to technology, we no longer have to be physically present at the office to be productive. Joining our company allows you to work anywhere without place-constraint.
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Team-building events
Our company simply cannot function well without teams of people working together. That said, we provide numerous team-building activities and events for you and your team to nurture meaningful relationships between every individual.
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Career growth
Ever feel stuck with your career? We don't hire you simply because we needed to fill an empty slot. Together, we will help you shape and grow your career so you can progress further and rediscover your true sense of purpose at work.
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Workstation assistance
In need of a laptop or certain devices specifically for work? It's on us. We will provide the necessary tools that you need so you can focus on what you do best and get a job done.
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Skills
Job description for Machine Learning Engineer at Pt Nomura Research Institute Indonesia
- Translate and refine business goals into appropriate machine learning objectives.
- Design and implement ML/DL solutions and integrate them with various Big Data platforms and architectures.
- Create and maintain ML pipelines that are scalable, robust, and ready for production.
- Collaborate with domain experts, software developers, and data scientists.
- Troubleshoot ML/DL model issues, including recommendations for retrain, re-validate, and improvements/optimization.
- Hands-on experience in building ML models deployed into real-world business applications or research.
- Working knowledge of ML/DL algorithms (classification, regression, clustering, hyperparameter tuning, etc).
- Proficiency with Python and libraries for machine learning such as scikit-learn and pandas.
- Good understanding of Deep learning frameworks such as Tensorflow, Keras, PyTorch, MXNet, etc.
- Experience in using computer vision libraries such as OpenCV, PIL.
- Experience working with cloud services platform (AWS or GCP) to build ML/DL pipelines.
- Experience in multi-GPU model training with CUDA.
- Experience in ML experiment tracking tools (e.g. WandB, Neptune, TensorBoard).
- Experience in model deployment using Docker (e.g. AWS SageMaker, Google Kubernetes Engine).
- Experience in model compression or quantization for on-edge-device inference.
- Experience with Continuous Integration and Continuous Delivery(CI/CD).
- Relevant certifications in machine learning and cloud technologies (e.g., AWS, Coursera) would be a plus.
Interview process
- Apply via Glints
- Screening interview with our in-house recruiter
- Aptitude test
- Technical test
- Interview with our Hiring Manager
- Final interview
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