Senior Data Scientist - VLM (LATAM)

Xometry is seeking a Senior Data Scientist to join our Generative AI team. The candidate will focus on training and fine-tuning Visual Language Models (VLMs) for multimodal document understanding. The ideal candidate will leverage their expertise in machine learning and computer vision to advance Xometry's capabilities in processing and extracting structured data from complex documents and images. This is a 1-year contract.

Responsibilities:

• Develop, fine-tune, and evaluate Visual Language Models (VLMs) to enhance document understanding, focusing on multimodal data such as text, images, and technical drawings.
• Design and implement data preparation, cleaning, and augmentation processes tailored to multimodal model training, ensuring high-quality data pipelines for VLMs.
• Leverage transfer learning and pre-trained models to accelerate model development and optimize performance on Xometry’s specific data.
• Use cloud resources (e.g., Amazon Web Services) to scale training and fine-tuning processes for VLMs efficiently.
• Collaborate with data engineering and machine learning operations (MLOps) teams to deploy VLMs into production and monitor their performance.
• Interpret model outputs and improve model accuracy and robustness by applying data analysis and visualization tools (such as Python, Jupyter Notebooks, and SQL).
• Experiment with and implement state-of-the-art model architectures, continuously optimizing VLM performance in a fast-paced, iterative environment.
• Work within a team-oriented setting, participating in peer reviews, sharing insights, and contributing to an environment of continuous learning and improvement.

Requirements:

• A bachelor’s degree is required; an advanced degree (M.S. or PhD) in computer science, data science, machine learning, or a related field is highly preferred.
• 5+ years of experience in data science and machine learning, with expertise in Visual Language Models or multimodal machine learning.
• Strong experience with machine learning libraries and frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Proficiency in Python, including libraries like pandas, numpy, and scikit-learn.
• Solid understanding of deep learning techniques and experience with transfer learning, fine-tuning, and model evaluation.
• Experience with cloud platforms (e.g., AWS SageMaker) for model training and deployment.
• Familiarity with data processing and visualization tools (SQL, Jupyter Notebooks, Looker, etc.) and basic database knowledge (e.g., Snowflake, MongoDB).
• Excellent analytical and problem-solving skills, with a strong ability to work in an environment that values teamwork, innovation, and continuous learning.
• Familiarity with computer vision tasks and frameworks, as well as experience with multimodal data, is a plus.

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