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Machine Learning Engineer Singapore London San Francisco

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Kaedim
Full Time position
Listed on 2025-11-19
Job specializations:
  • Software Development
    Machine Learning/ ML Engineer, AI Engineer, Data Scientist, Data Engineer
Salary/Wage Range or Industry Benchmark: 120000 - 160000 USD Yearly USD 120000.00 160000.00 YEAR
Job Description & How to Apply Below

As a Machine Learning Engineer, you will play a key role in advancing our machine learning models that power our 2D-to-3D pipeline. You’ll be responsible for developing, optimizing, and deploying ML algorithms that enhance the efficiency and accuracy of our AI tools, enabling us to deliver production-ready 3D models faster than ever.

Responsibilities

Collaborate with cross-functional teams, including software engineers and 3D artists, to design, develop, and implement machine learning models for image-to-3D model conversion.

Build and optimize machine learning algorithms for computer vision, deep learning, and generative models, improving the speed and quality of 3D model generation.

Work on model optimization and scaling to ensure the robustness of AI solutions and manage the efficient processing of large-scale data.

Conduct continuous research and stay up-to-date with the latest machine learning advancements, applying them to improve the technology stack.

Continuously test and evaluate models, fine-tuning them based on performance feedback and customer needs.

Requirements

Strong experience in machine learning and deep learning techniques, with a focus on computer vision, image processing, or generative models.

Proficiency in Python and machine learning libraries such as Tensor Flow, PyTorch, or Keras.

Solid experience with data preprocessing, cleaning, and augmentation for training machine learning models.

Hands-on experience with model deployment and versioning

Strong understanding of machine learning frameworks and optimization techniques to ensure the scalability and efficiency of models.

Experience with cloud platforms (e.g., AWS, GCP, Azure) for deploying machine learning models and managing large datasets.

Experience in developing and presenting proof of concepts (PoCs) to demonstrate the feasibility and potential of machine learning solutions for real-world applications.

Strong problem-solving skills and the ability to troubleshoot complex issues in model development and deployment.

Excellent collaboration and communication skills to work with cross-functional teams and effectively communicate complex concepts to non-technical stakeholders.

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