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Machine Learning Engineer

Talent Safari

🌍 Africa 🏠 Remote ⏱ Part-time 💼 Mid-level 🗓 4 days ago

ABOUT THE COMPANY

Lima Labs https://www.lima.ag/about is an agri-tech company operating at the intersection of farming, data, and intelligent technology. Inspired by the Kiswahili words mkulima (farmer) and ukulima (agriculture), we exist to empower growers with the tools and insights they need to make better decisions and run more productive, sustainable farms.

We are a tech-first, data-driven team building AI-powered solutions that turn complex farm data into clear, actionable insights—from crop health and yield forecasting to quality and sales strategy. By simplifying farm management and translating data into decisions that matter, we help growers “know more so they can grow more,” while working in harmony with nature to drive long-term, sustainable productivity.

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.

WHAT YOU WILL DO

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 ...

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