Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A rapidly growing agricultural technology organization is developing AI-powered solutions designed to improve farming efficiency, crop quality, and sustainability. Its technology combines tractor-mounted camera systems, computer vision, edge AI, and farm-management software to collect and analyze detailed information about crops, including yield estimates, fruit size, crop health, and disease.
Key Responsibilities
- Build and maintain scalable ETL pipelines for processing large and diverse image datasets collected from tractor-mounted camera systems.
- Develop and deploy infrastructure for model training, evaluation, and inference across both cloud environments and edge devices.
- Design and implement intelligent active-sampling infrastructure to optimize data collection and improve machine learning model performance.
- Stay current with developments in computer vision models and architectures and apply relevant advancements to production systems.
- Collaborate with multidisciplinary teams to integrate machine learning solutions into production robotics systems.
- Work closely with agronomists and farmers to understand crop biology and translate domain knowledge into actionable machine learning features.
- Support various areas of the software stack as a generalist when required.
- Contribute to the development of robust machine learning systems capable of operating on large-scale, real-world agricultural datasets.
- Help improve the reliability, scalability, and efficiency of machine learning infrastructure and deployment processes.
Required Qualifications
- 2+ years of professional industry experience building production-grade data pipelines and machine learning infrastructure.
- Proficiency in Python.
- Experience with machine learning frameworks such as PyTorch.
- Strong experience with data engineering tools such as Pandas, SQL, MLFlow, and Weights & Biases.
- Familiarity with cloud platforms such as AWS and GCP.
- Experience with containerization technologies such as Docker and Kubernetes.
- Experience working with massive amounts of real-world training data.
- Familiarity with MLOps practices and data engineering processes that support consistent machine learning model deployment.
- Ability to work independently and learn quickly in a dynamic environment.
- Strong problem-solving skills and the ability to develop practical solutions to complex technical challenges.
- Enthusiasm for taking on multiple roles and responsibilities within a growing organization.
Preferred Skills and Attributes
- Experience working with computer vision systems or image-based machine learning.
- Experience deploying machine learning models to edge devices.
- Familiarity with robotics or production robotics systems.
- Understanding of agricultural technology, crop science, or related domains.
- Strong interest in applying machine learning to real-world problems.
- Ability to collaborate effectively with engineers, agronomists, farmers, and other stakeholders.
- Willingness to work closely with field teams and gain hands-on understanding of agricultural operations.
Work Environment and Culture
The organization values resilience, rapid execution, continuous improvement, customer focus, and a low-ego approach to teamwork. Team members are encouraged to take ownership of outcomes, move quickly, continuously improve systems, and work directly with customers in the field.
The role provides an opportunity to work closely with a highly driven team and contribute directly to technology designed to reduce food waste and improve agricultural productivity.
Disclaimer
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