Machine Learning Engineer

TerraClear applies artificial intelligence, robotics, and mechanical design to enhance agricultural intelligence. As a Machine Learning Engineer, you will design, train, and deploy deep learning models for computer vision applications, collaborating with cross-functional teams to improve model performance in real-world settings.

Responsibilities:

• Design and train modern computer vision models (CNNs, Vision Transformers, foundation models) to solve novel perception tasks
• Build and maintain scalable training pipelines using PyTorch and HPC infrastructure (e.g., Slurm, distributed training)
• Develop data curation and active learning workflows
• Optimize models for deployment (ONNX, TensorRT, containerization)
• Test and validate models in both cloud and edge environments
• Build reproducible experimentation workflows (version control, experiment tracking, configuration management)
• Drive experimental cycles: define hypotheses, implement techniques from literature, evaluate results, and present recommendations
• Translate research ideas into production-ready implementations
• Design, implement, and test ML-related components and supporting software
• Perform statistical analysis and fine-tune model performance based on production and field feedback

Requirements:

• 3+ years of full-time professional experience in computer vision-focused machine learning (not student internship)
• Strong PyTorch experience, including custom layers, loss functions, datasets, dataloaders, and training loops
• Experience with modern vision architectures (CNNs, ViTs, DETR-style models, foundation models)
• Experience building or contributing to production ML systems
• Solid software engineering fundamentals (testing, version control, clean code principles)
• Strong communication skills and ability to explain complex technical topics clearly
• Engineering degree or equivalent practical experience
• Experience deploying models to edge devices (Jetson, embedded GPUs, mobile platforms)
• Experience with AWS or similar cloud infrastructure
• Experience with Docker and containerized ML workflows
• Familiarity with robotics or perception systems
• Experience owning or contributing to a production model that delivered business value