Machine Learning Engineer

About Moultrie:

Moultrie ( www.moultrie.com ) has been a trusted name in game feeders and wildlife cellular camera innovation for decades, and we're just getting started! Headquartered in Birmingham, Alabama, we're a team of hunters, engineers, and innovators building the tools that connect people to the outdoors. We take pride in developing deep user understanding, obsessing about the details, and going the extra mile to show our users we love them. From cellular cameras to smart feeders, our products live in the field — and so does our drive to make them better.

If you're someone who holds yourself to a high standard, moves with urgency, and wants your work to matter, you'll fit right in here!

We're guided by a clear set of principles: Customer Obsession. Excellence is the Standard. Bias for Action. Act Boldly. Deliver Results. Hire and Develop the Best. Be Curious and Learn. Win as a Team.

About the Role

You will collaborate on the entire ML pipeline — from species categorization to tagged camera images to deer movement predictions…all the way to hunt location optimization. You will design, train, and deploy the models that turn behavioral data into actionable stand recommendations for hunters. This is a high-impact, high-autonomy role that will define the technical direction of the platform.

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.

Qualifications

• 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).

Required Skills

• 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.

Preferred Skills

• 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.