Applied Computer Vision Engineer - Data Driven Development

As an Applied Computer Vision Engineer focused on Data Driven Development, you will be a critical part of our feature team, responsible for building and scaling the infrastructure that powers our advanced perception systems.

You will architect and implement the robust data pipelines and cloud-native workflows that enable the development, training, and evaluation of next-generation computer vision models. This role is ideal for an engineer passionate about data systems, MLOps, and enabling large-scale machine learning.

Responsibilities

• Design, implement, and maintain scalable data pipelines and cloud-based workflows for computer vision applications.
• Architect efficient database schemas, queries, and end-to-end data pipelines to support large-scale data operations.
• Develop and scale Azure-based data pipelines to manage high-throughput workloads, including the processing, versioning, and management of millions of images.
• Build, automate, and manage robust training and evaluation pipelines using frameworks like PyTorch and TensorFlow.
• Integrate ML pipelines seamlessly with cloud storage and compute services, particularly Azure Blob Storage and Azure ML Compute.
• Analyze existing models and data workflows to identify and implement performance improvements and optimizations.
• Implement best practices for data quality, monitoring, and infrastructure-as-code.
• Document system architecture, data flows, and operational procedures.

Required Qualifications

• Education: Bachelor’s degree in Computer Science, Electrical Engineering, or a related field.
• Experience: 6 to 10 years Core Skills: Proven experience designing and implementing scalable data pipelines and cloud-based workflows.
• Strong proficiency with cloud platforms, specifically Azure (AzureML, Azure Blob Storage, Azure ML Compute).
• Hands-on experience with data processing and orchestration tools such as Databricks or Ray.
• Proficiency in containerization technologies, especially Docker.
• Experience building or maintaining training/evaluation pipelines using PyTorch or TensorFlow.
• Experience with or exploration of data visualization and analysis tools (e.g., Voxel).

Programming

Proficiency in Python and a strong understanding of object-oriented programming.

Tools

Familiarity with essential software development tools like Git and working in a Linux environment.

Additional Qualifications Master’s degree in a relevant field. Prior experience in the ADAS, autonomous driving, or robotics domains.

Experience with other cloud platforms (e.g., AWS, GCP). Knowledge of model optimization and deployment (e.g., TensorRT, ONNX). Hands-on experience with C++ is beneficial.