Deep Learning Engineer | Indiananapolis | Ag-Tech

Industry – Ag-Tech

Description

We are working alongside an agriculture analytics company turning multi-source data, including aerial imagery and complex datasets into decisions for growers. We’re hiring a Machine Learning Engineer to architect and deliver deep learning solutions in production. You will own end-to-end ML pipelines, reducing deployment time and the effort required to develop, test, and ship models from research to production, while partnering across teams to make our products reliable at scale.

The client

Our client are an established Ag-Tech company, providing growers insights and agronomic data which allow them to increase their yields and reduce costs.

Location

Indianapolis - Hybrid 3 Days On Site

Key Responsibilities:

Design, implement, and optimize deep learning models for remote sensing applications.
Research and experiment with Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) for large-scale image understanding.
Explore multimodal learning approaches by integrating imagery with additional geospatial/temporal datasets.
Build and maintain reproducible ML pipelines following MLOps best practices.
Leverage PyTorch and PyTorch Lightning for scalable model development and training.
Preprocess, analyze, and manage large-scale remote sensing datasets (satellite, aerial, multispectral, hyperspectral).
Collaborate with cross-functional teams on research-to-production workflows.
Contribute to publications, reports, and presentations showcasing novel findings.

Requirements/Qualifications:

Strong background in deep learning with expertise in computer vision.
Hands-on experience with PyTorch (PyTorch Lightning is a plus).
Solid knowledge of CNNs, Vision Transformers, and GNNs.
Experience working with remote sensing imagery and geospatial datasets.
Familiarity with multimodal research (e.g., combining imagery, text, or tabular data).
Understanding of MLOps principles: reproducibility, versioning, deployment, monitoring.
Proficiency in Python, data handling libraries (NumPy, Pandas), and image processing tools.
Strong problem-solving skills, research mindset, and ability to work independently.
(Preferred) Track record of publications in ML/AI/remote sensing conferences/journals.
(Preferred) Experience with self-supervised learning or foundation models.
(Preferred) Familiarity with distributed training and high-performance computing environments.
(Preferred) Exposure to geospatial libraries (GDAL, Rasterio, GeoPandas).

Mackinnon Bruce International

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