Machine Learning Engineer 3

We are seeking a Machine Learning Engineer to build production-grade computer vision pipelines and manage end-to-end ML data infrastructure for autonomous aerial systems. You will own the full lifecycle from data curation through model deployment on edge hardware.

Responsibilities:

ML Model Development & Deployment

• Design, train, and optimize object detection and tracking models (YOLO, RT-DETR, ViT) for aerial platforms

• Deploy models on NVIDIA Jetson hardware using TensorRT, achieving real-time inference

• Integrate vision models into ROS2 autonomy stacks with efficient message passing and synchronized sensor data

• Profile and optimize model performance to meet compute, power, and latency constraints on embedded systems

Data Infrastructure & MLOps

• Build and maintain data pipelines for ingestion, labeling, versioning, and quality control of large-scale aerial imagery datasets

• Design active learning loops and data selection strategies to maximize model performance with minimal labeling effort

• Implement automated model evaluation frameworks and performance monitoring for deployed systems

• Create synthetic data generation pipelines and data augmentation strategies to improve model robustness

System Integration

• Develop ROS2 nodes for real-time sensor processing, model inference, and downstream autonomy integration

• Work with GStreamer and Jetson multimedia APIs for efficient camera pipeline management

• Collaborate with perception and autonomy teams to ensure vision outputs meet system requirements

Qualifications:

• Bachelor's in Computer Science, Electrical/Computer Engineering, Robotics, or related field (Master's preferred) – equivalent industry experience also welcome

• 4-6+ years of experience in machine learning with focus on computer vision applications

• Strong proficiency in PyTorch and modern deep learning frameworks

• Hands-on experience deploying models on NVIDIA Jetson platforms (Orin, Xavier, or similar)

• Solid understanding of model optimization techniques: quantization, pruning, TensorRT, ONNX

• Experience with ROS2 for robotics applications, including sensor integration and message handling

• Proven track record building and managing ML data pipelines and infrastructure

• Strong Python skills with experience in data processing libraries (NumPy, Pandas, OpenCV)

• Familiarity with MLOps tools and practices (versioning, experiment tracking, CI/CD for models)

Preferred:

• Experience with C++ for performance-critical vision processing

• Knowledge of CUDA programming or GPU optimization

• Familiarity with data labeling tools and annotation management systems

• Experience with synthetic data generation or domain adaptation techniques

• Understanding of aerial imagery characteristics and challenges

• Background in object tracking algorithms (Kalman filters, Hungarian assignment, multi-object tracking)

• Experience with GStreamer, Jetson BSP, or embedded Linux development

• Knowledge of model monitoring and performance debugging in production systems

• Familiarity with DVC, MLflow, Weights & Biases, or similar MLOps platforms

#LI-BC1