Select your current position
Pick a role and level — we'll show the growth path, skills and gap analysis.
Development path
Junior
0-2 years
Responsibility: Completing tasks under the guidance of senior colleagues. Learning the codebase, standards, and team processes. Writing code to spec, fixing simple bugs, writing tests.
Key skills:
Middle
2-5 years
Responsibility: Independently developing features from decomposition to deployment. Participating in code review. Optimizing performance. Mentoring junior developers. Taking part in architecture discussions.
Key skills:
Senior
5-8 years
Responsibility: Designing the architecture of components and services. Solving complex technical problems. Managing technical debt. Code review as a quality gatekeeper. Mentoring middle developers. Choosing technologies for new tasks.
Key skills:
Lead / Staff
7-12 years
Responsibility: Technical leadership of a team or area. Designing system architecture. Coordinating with other teams. Establishing standards and best practices. Participating in hiring. Planning the technical roadmap.
Key skills:
Principal
10+ years
Responsibility: Technical strategy at the company or domain level. Cross-organizational influence. Solving systemic business problems through technology. Mentoring lead engineers. Publicly representing the company.
Key skills:
Gap analysis: skills to develop
To reach the next level you'll need to develop:
Independently builds scikit-learn pipelines for HOG/SIFT feature extraction with SVM and ensemble classifiers for image recognition. Understands trade-offs between model complexity and inference speed. Tunes hyperparameters via GridSearchCV for optimal precision-recall.
Independently designs gRPC services for computer vision pipelines — streaming image data with bidirectional gRPC, efficient proto schemas for bounding box and segmentation results, and batched inference request patterns. Writes comprehensive proto documentation and integration tests for vision service contracts.
Builds end-to-end ML pipelines for vision models: dataset versioning, distributed training, hyperparameter tuning, and model registry integration. Implements data quality checks for image datasets.
Designs MLflow experiment structures for multi-stage CV pipelines: augmentation tuning, backbone selection, and detection head optimization. Implements custom metrics logging for CV tasks (dice scores, MOTA). Configures Model Registry for versioning production CV models.
Implements CV model serving pipelines: batch and real-time inference with GPU optimization, model ensemble strategies for accuracy improvement, and pre/post-processing pipeline optimization. Configures TensorRT/ONNX Runtime for inference acceleration. Implements model caching and warm-up strategies for consistent latency.
Implements CV data management pipelines with Pandas: annotation format conversion (COCO, YOLO, VOC), dataset statistics computation for class imbalance analysis, and batch metadata processing for training monitoring. Integrates Pandas with image processing libraries for efficient data loading. Optimizes DataFrame operations for large annotation datasets.
Independently implements tasks with Python Web Frameworks in PyTorch/OpenCV. Understands internals and optimizes performance. Writes tests using evaluation metrics.
Implements detection (YOLO, SSD) and segmentation (U-Net, Mask R-CNN) in PyTorch. Fine-tunes pretrained models with custom heads and layer freezing. Builds augmentation pipelines with Albumentations. Processes video with efficient batching and temporal models.
Independently implements tasks with S3 / Object Storage in PyTorch/OpenCV. Understands internals and optimizes performance. Writes tests using evaluation metrics.
Independently implements tasks with Task Queues in PyTorch/OpenCV. Understands internals and optimizes performance. Writes tests using evaluation metrics.
Independently applies Video Analytics in practice. Understands trade-offs of different approaches. Solves typical tasks independently.
Independently applies Object Detection in practice. Understands trade-offs of different approaches. Solves typical tasks independently.
Independently configures model monitoring for computer vision systems — tracks distribution shifts in input images, bounding box accuracy degradation, and segmentation quality. Sets up Evidently or Prometheus-based dashboards for real-time detection of concept drift across visual inference pipelines.
Independently applies Computer Vision Fundamentals in practice. Understands trade-offs of different approaches. Solves typical tasks independently.
Independently applies Image Segmentation in practice. Understands trade-offs of different approaches. Solves typical tasks independently.
Independently structures experiment tracking for CV pipelines: versions datasets with DVC, logs augmentation configs and model architectures in W&B, and builds comparison dashboards for detection/segmentation metrics
Manages memory for image and video processing pipelines: buffer allocation, zero-copy operations, and GPU memory transfers. Understands OpenCV Mat memory layout and reference counting. Handles large tensor allocations for inference models. Profiles memory usage in real-time processing scenarios.