Machine Learning Engineer

99; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

A leading company in the real estate sector is seeking a Machine Learning Engineer to design and deploy innovative solutions for home price estimation, personalized recommendations, and predictive analytics using advanced technologies. This role offers the chance to work with collaborative teams and impact real-time decision-making in the industry.

Qualifications

• 3+ years of experience in machine learning or a similar role.
• Proficiency in Python and libraries like TensorFlow and PyTorch.
• Hands-on experience deploying models in Docker and Kubernetes.

Responsibilities

• Design and deploy machine learning models for various tasks.
• Collaborate with data scientists and optimize models.
• Implement model serving pipelines using Kubernetes.

Skills

Machine Learning

Python

TensorFlow

Scikit-learn

Docker

Kubernetes

Data Processing

RESTful APIs

MLOps

Image Recognition

Tools

Airflow

Kubeflow

AWS

GCP

Azure

Job description

At 99 Group, we're on a mission to revolutionize the real estate industry by seamlessly integrating cutting-edge technology and innovation. If you're excited to be at the forefront of this transformation and are ready to tackle the industry's challenges head-on, we want you to be a part of our dynamic team.

As a Machine Learning Engineer, you will be responsible for building and deploying machine learning models that solve complex problems in home price estimation, natural language processing, listings recommendation and image/object detection. You will collaborate closely with data science and engineering teams to ensure our models are not only accurate but also scalable and production-ready. Your work will directly impact our ability to make intelligent, data-driven decisions and provide real-time predictions to our users.

What You'll Be Doing

• Design, develop, and deploy machine learning models for real-time regression, classification, and object detection tasks.
• Collaborate with data scientists to build, train, and optimize models using frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Implement model serving pipelines for deployment in Kubernetes environments, using Docker and orchestration tools.
• Develop and maintain scalable machine learning APIs to serve predictions and integrate them with client applications.
• Automate data preprocessing, feature extraction, and model training pipelines using tools like Airflow, Argo, Kubeflow, or similar.
• Monitor the performance of deployed models and implement updates to maintain accuracy and efficiency over time.
• Work closely with DevOps/Engineering to ensure smooth model integration into production environments.
• Conduct code reviews, write unit tests, and maintain high code quality and documentation.
• Stay updated with the latest research and advancements in machine learning and AI technologies.
  
  

What We're Looking For

• 3+ years of experience in machine learning, model deployment, or a similar role.
• Proficiency in Python, with experience in libraries like TensorFlow, PyTorch, and Scikit-learn.
• Strong knowledge of data processing frameworks (Pandas, Numpy) and experience working with large-scale datasets, both structured and unstructured.
• Hands-on experience in deploying machine learning models in production using Docker and Kubernetes.
• Experience in building and deploying RESTful APIs for machine learning models.
• Experience working with cloud platforms (AWS, GCP, or Azure) for model deployment and scaling.
• Familiarity with Git and CI/CD pipelines.
• Bonus: Experience with MLOps tools like Airflow, Argo, Kubeflow, or MLflow for workflow automation and monitoring.
• Experience in image recognition and object detection using CNNs, YOLO, or similar architectures.
• Familiarity with distributed systems and parallel processing (e.g., Spark, Dask).
  
  

Are you ready to shape the future of real estate? Join us in this exciting journey of innovation, collaboration, and growth. Together, let's redefine what's possible in the world of real estate.

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