AI Engineer, Computer Vision

📋 Description

• Build end-to-end ML training pipeline: data ingestion, labeling, training, and retraining.
• Train and evaluate segmentation, classification, and mass-estimation models.
• Create cloud-side evaluation harness for real-world model performance.
• Own MLOps: tracking experiments, versions, and automated evaluation.
• Export/validate models for edge deployment; optimize/quantize with edge team.
• Analyze failure cases across foods and environments to drive improvements.

🎯 Requirements

• CV fundamentals: segmentation, detection, classification, tracking.
• Fluency with VLMs, LLMs, foundation models and agent systems.
• Experience building ML pipelines and annotation systems.
• Experience evaluating ML models; metric-driven decisions.
• Cloud ML infra (AWS or equivalent) and production pipelines.
• Cloud-to-edge model deployment familiarity.

🎁 Benefits

• Video understanding (temporal tracking, video segmentation).
• Foundation models for data annotation.
• MLOps tooling (Weights & Biases, MLflow, SageMaker).
• Shipping LLM/agent-powered features in products.
• Hardware/IoT CV experience for embedded cameras.