Note: The job is a remote job and is open to candidates in USA. Moultrie is a Birmingham-based wildlife technology company developing cellular cameras, smart feeders, and tools that connect people to the outdoors. The Machine Learning Engineer will design, train, deploy, and monitor computer vision and behavioral prediction models across the end-to-end ML pipeline, turning wildlife data into actionable hunting recommendations.
Responsibilities
- Design and train object detection and classification models (YOLOv8, RT-DETR, or similar) to identify deer presence, sex, age class, and antler characteristics in trail camera imagery
- Build and maintain the end-to-end ML pipeline: data ingestion from cloud storage, preprocessing, model training on GPU clusters, evaluation, and deployment via Triton, TorchServe, or similar
- Develop individual deer re-identification models using coat patterns and antler morphology to track specific animals across cameras and time
- Engineer features from vision outputs and environmental data (weather, terrain, moon phase, rut calendar) to feed downstream behavioral prediction models
- Integrate ML Ops tooling — MLflow or Weights & Biases — for experiment tracking, model versioning, and staged production deployments
- Collaborate with Data Engineering to optimize data pipelines and with the Wildlife Biologist advisor to validate model outputs against real-world deer behavior
- Monitor model performance in production and implement retraining pipelines to address data drift over seasons
Skills
- • 4+ years of experience in machine learning engineering with demonstrated production deployments
- • Deep proficiency in PyTorch; experience with Ultralytics/YOLO or similar detection frameworks strongly preferred
- • Solid understanding of CNN architectures, transfer learning, and domain adaptation
- • Experience deploying models at scale on GPU infrastructure (AWS SageMaker, GCP Vertex AI, or equivalent)
- • Proficiency in Python and familiarity with data pipeline tooling (Kafka, Airflow, or similar)
- • Strong fundamentals in ML evaluation — confusion matrices, mAP, precision/recall tradeoffs — and the ability to diagnose model failures
- • Familiarity with time-series prediction models (LSTMs, Prophet, XGBoost for temporal data)
- • Deep proficiency in PyTorch
- • Experience with Ultralytics/YOLO or similar detection frameworks
- • Solid understanding of CNN architectures, transfer learning, and domain adaptation
- • Experience deploying models at scale on GPU infrastructure
- • Proficiency in Python
- • Familiarity with data pipeline tooling
- • Strong fundamentals in ML evaluation
- • Familiarity with time-series prediction models
- • Experience with Ultralytics/YOLO or similar detection frameworks strongly preferred
- • Experience with re-identification (ReID) or few-shot learning tasks
- • Prior work on wildlife imagery, agricultural computer vision, or similar low-contrast, occlusion-heavy domains
- • Experience with Microsoft Azure
- • Passion for the outdoors or hunting is a genuine plus — domain empathy makes better products
Company Overview