{"schemaVersion":"jobsearcher.job.v1","id":"43dfbf51d5414f72d947715e","url":"https://jobsearcher.com/jobs/43dfbf51d5414f72d947715e","canonicalUrl":"https://jobsearcher.com/jobs/43dfbf51d5414f72d947715e","title":"Machine Learning Engineer","description":"Headquartered in Birmingham, Alabama, Moultrie (www.moultrie.com) is the leader in game feeders and cellular camera innovation, building products used by hunters, property owners, and others for real-time remote monitoring.\n\nWe take pride in developing deep user understanding, obsessing about the details, and going the extra mile to show our users we love them. Moultrie is customer-driven – hardware, software, marketing, and customer success teams collaborate to deliver a quality user experience.\n\nWe are guided by the following 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\n\nJob Summary\n\nAs a Machine Learning Engineer, you will assist in owning the prediction ML lifecycle, from tagged camera images to deer movement predictions and hunt location optimization. You will design, train, and deploy the prediction layer that turns 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.\n\nJob Responsibilities\nDesign 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.\nBuild 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.\nDevelop individual deer re-identification models using coat patterns and antler morphology to track specific animals across cameras and time.\nEngineer features from vision outputs and environmental data (weather, terrain, moon phase, rut calendar) to feed downstream behavioral prediction models.\nIntegrate ML Ops tooling - Mlflow or Weights & Biases - for experiment tracking, model versioning, and staged production deployments.\nCollaborate with Data Engineering to optimize data pipelines and with the Wildlife Biologist advisor to validate model outputs against real-world deer behavior.\nMonitor model performance in production and implement retraining pipelines to address data drift over seasons.\n\nJob Requirements\n4+ years of experience in machine learning engineering with demonstrated production deployments.\nDeep proficiency in PyTorch; experience with Ultralytics/YOLO or similar detection frameworks strongly preferred.\nSolid understanding of CNN architectures, transfer learning, and domain adaptation.\nExperience deploying models at scale on GPU infrastructure (AWS SageMaker, GCP Vertex Al, or equivalent).\nProficiency in Python and familiarity with data pipeline tooling (Kafka, Airflow, or similar).\nStrong fundamentals in ML evaluation - confusion matrices, mAP, precision/recall tradeoffs and the ability to diagnose model failures.\nFamiliarity with time-series prediction models (LSTMs, Prophet, XGBoost for temporal data).\n\nEssential Job Function\nExperience with re-identification (RelD) or few-shot learning tasks.\nPrior work on wildlife imagery, agricultural computer vision, or similar low-contrast, occlusion-heavy domains.\nExperience with Microsoft Azure.\nPassion for the outdoors or hunting is a genuine plus - domain empathy makes better products.\n\nWe are an equal opportunity employer and comply with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, sex, pregnancy status, age, national origin or ancestry, ethnicity, religion, creed, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.\n\nWe comply with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local law.","company":"Pradco","rawCompany":"pradco","city":"Brookline","state":"MA","isRemote":false,"isActive":false,"createdAt":"2026-08-25T12:19:23.490Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"Headquartered in Birmingham, Alabama, Moultrie (www.moultrie.com) is the leader in game feeders and cellular camera innovation, building products used by hunters, property owners, and others for real-time remote monitoring.\n\nWe take pride in developing deep user understanding, obsessing about the details, and going the extra mile to show our users we love them. Moultrie is customer-driven – hardware, software, marketing, and customer success teams collaborate to deliver a quality user experience.\n\nWe are guided by the following 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\n\nJob Summary\n\nAs a Machine Learning Engineer, you will assist in owning the prediction ML lifecycle, from tagged camera images to deer movement predictions and hunt location optimization. You will design, train, and deploy the prediction layer that turns 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.\n\nJob Responsibilities\nDesign 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.\nBuild 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.\nDevelop individual deer re-identification models using coat patterns and antler morphology to track specific animals across cameras and time.\nEngineer features from vision outputs and environmental data (weather, terrain, moon phase, rut calendar) to feed downstream behavioral prediction models.\nIntegrate ML Ops tooling - Mlflow or Weights & Biases - for experiment tracking, model versioning, and staged production deployments.\nCollaborate with Data Engineering to optimize data pipelines and with the Wildlife Biologist advisor to validate model outputs against real-world deer behavior.\nMonitor model performance in production and implement retraining pipelines to address data drift over seasons.\n\nJob Requirements\n4+ years of experience in machine learning engineering with demonstrated production deployments.\nDeep proficiency in PyTorch; experience with Ultralytics/YOLO or similar detection frameworks strongly preferred.\nSolid understanding of CNN architectures, transfer learning, and domain adaptation.\nExperience deploying models at scale on GPU infrastructure (AWS SageMaker, GCP Vertex Al, or equivalent).\nProficiency in Python and familiarity with data pipeline tooling (Kafka, Airflow, or similar).\nStrong fundamentals in ML evaluation - confusion matrices, mAP, precision/recall tradeoffs and the ability to diagnose model failures.\nFamiliarity with time-series prediction models (LSTMs, Prophet, XGBoost for temporal data).\n\nEssential Job Function\nExperience with re-identification (RelD) or few-shot learning tasks.\nPrior work on wildlife imagery, agricultural computer vision, or similar low-contrast, occlusion-heavy domains.\nExperience with Microsoft Azure.\nPassion for the outdoors or hunting is a genuine plus - domain empathy makes better products.\n\nWe are an equal opportunity employer and comply with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, sex, pregnancy status, age, national origin or ancestry, ethnicity, religion, creed, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.\n\nWe comply with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local law.","datePosted":"2026-08-25T12:19:23.490Z","dateModified":"2026-08-25T12:19:23.490Z","hiringOrganization":{"@type":"Organization","name":"Pradco","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Brookline","addressRegion":"MA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"43dfbf51d5414f72d947715e"},"url":"https://jobsearcher.com/jobs/43dfbf51d5414f72d947715e"}}