Machine Learning Engineer Job Lima Lab

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About the Role

Lima is looking for a Machine Learning Engineer to design and implement solutions in agriculture. If you are passionate about data science and computer vision, converting data into commercial value, then you’re in the right place.

Key Responsibilities

Core Tasks (80%):

• Design, develop, and deploy computer vision models for tasks such as object detection, segmentation, classification, and tracking.
• Work closely with data scientists and researchers to experiment with state-of-the-art deep learning architectures (e.g., CNNs, Vision Transformers).
• Build and maintain efficient data pipelines for image and video data, including preprocessing, augmentation, and annotation workflows.
• Optimize models for performance, scalability, and deployment on various platforms (cloud, edge, or mobile).
• Conduct experiments, perform model evaluation, and analyze results to guide iterations.
• Collaborate with product and engineering teams to integrate models into production systems.
• Stay current with advances in computer vision and machine learning research and propose innovative approaches.
• Document models, experiments, and system architecture for reproducibility and knowledge sharing.

Build Beyond the Core (20%):

• Set and grow toward your ideal career goals
  • What are you passionate about?
  • What do you want to learn, and where else would you like to contribute?

Qualifications & Experience

• A Bachelorʼs or Masterʼs degree in Computer Science, Electrical Engineering, or a related field (PhD is a plus).
• 3+ years of hands-on experience in machine learning, with at least 2 years focusing on computer vision applications.
• Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, or JAX).
• Solid understanding of deep learning architectures such as CNNs, RNNs, and Transformers, and experience applying them to visual tasks.
• Experience with large-scale image/video datasets and related tools (e.g., OpenCV, Albumentations, COCO format).
• Proficiency in model optimization and deployment techniques (ONNX, TensorRT, quantization, pruning).
• Familiarity with MLOps tools and workflows for training, tracking, and deploying models.
• Strong foundation in mathematics, linear algebra, and probability.
• Excellent problem-solving skills, curiosity, and ability to work in interdisciplinary teams.

How to Apply

Click Here to Apply