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.