enior Machine Learning Engineer - Computer Vision & Perception

Senior Machine Learning Engineer - Computer Vision & Perception

Job Summary

We are seeking a highly skilled Senior Machine Learning Engineer with expertise in Computer Vision Perception Systems and Sensor Data Processing to develop and deploy machine learning solutions for autonomous and AI-driven applications. The ideal candidate will have hands-on experience working with camera and LiDAR data building scalable data pipelines improving annotation quality generating ground truth datasets and training computer vision models.

The role requires close collaboration with data engineering annotation operations AI research and quality teams to deliver high-quality datasets and robust machine learning models that support perception-based products.

Key Responsibilities

Machine Learning & Model Development

• Train fine-tune evaluate and deploy machine learning and deep learning models for computer vision and perception applications.
• Develop and optimize model evaluation frameworks using appropriate performance metrics.
• Analyze model failures identify root causes and implement corrective actions to improve model accuracy and reliability.
• Support production deployment and monitoring of machine learning models.

Data Engineering & Automation

• Design and develop automated workflows for sensor-data ingestion curation sampling validation and release.
• Build scalable data pipelines for processing large-scale camera and LiDAR datasets.
• Automate data quality validation and model evaluation workflows using Python.
• Implement version control experiment tracking and dataset lifecycle management practices.

Perception Data & Ground Truth Generation

• Design and manage camera and LiDAR data collection pipelines.
• Develop ground truth generation frameworks for perception model training and validation.
• Create data preparation workflows supporting object detection tracking segmentation classification and sensor fusion applications.
• Maintain high-quality datasets for training validation and testing purposes.

Annotation Quality & Dataset Management

• Define annotation guidelines acceptance criteria and quality standards.
• Develop automated checks for annotation completeness consistency and accuracy.
• Perform root-cause analysis of labeling defects and implement quality improvement initiatives.
• Collaborate with annotation teams to improve labeling efficiency and dataset reliability.
• Measure and report annotation quality metrics and dataset health indicators.

Reporting & Stakeholder Collaboration

• Create dashboards and reports covering:
  • Dataset health and coverage
  • Annotation quality metrics
  • Model performance trends
  • Workflow efficiency and throughput
• Partner with Data Engineering AI/ML Quality Assurance and Operations teams to ensure successful project delivery.
• Present findings and recommendations to technical and non-technical stakeholders.

Required Qualifications

Technical Skills

• Strong experience in training fine-tuning and deploying machine learning models.
• Hands-on experience with computer vision and perception systems.
• Experience working with camera LiDAR and multimodal sensor datasets.
• Strong Python programming skills.
• Experience with machine learning frameworks such as:
  • PyTorch
  • TensorFlow
  • Scikit-learn
• Experience developing data validation and automation pipelines.
• Knowledge of:
  • Computer Vision
  • Object Detection
  • Image Segmentation
  • Object Tracking
  • Sensor Fusion
  • Deep Learning
• Familiarity with MLOps practices experiment tracking model versioning and deployment frameworks.
• Experience with Git and software development best practices.

Data Annotation & Quality

• Experience managing data annotation projects.
• Ability to define annotation workflows and quality assurance processes.
• Expertise in identifying labeling defects and improving dataset quality.
• Knowledge of annotation tools such as CVAT Labelbox Supervisely or similar platforms.

Preferred Qualifications

• Experience in Autonomous Driving Robotics ADAS or Intelligent Transportation Systems.
• Knowledge of point cloud processing and 3D perception.
• Experience with cloud platforms such as AWS Azure or Google Cloud.
• Familiarity with MLOps tools such as MLflow Weights & Biases Kubeflow or SageMaker.
• Experience with large-scale distributed data processing frameworks.