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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and optimizing machine learning models for computer vision applications, including object detection and classification. Proficient in deploying models in production environments and collaborating with cross-functional teams to meet customer requirements.
Highest-signal resume keywords
Machine Learning Systems DevelopmentComputer Vision Model DeploymentPython ProgrammingPyTorch or TensorFlow ExperienceModel Optimization Techniques
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Design, train and fine-tune models for object detection, classification, segmentation, tracking and anomaly detection on real customer video and images
- Adapt pretrained and foundation models, including YOLO-family detectors, ViTs, CLIP-style and vision-language models, to new customer use cases with limited labeled data
- Own data pipelines end to end, including curation, labeling guidelines, augmentation, synthetic data and dataset versioning
- Optimize models for production and edge hardware using quantization, pruning, distillation, ONNX export and TensorRT
- Build evaluation suites with metrics such as mAP, precision/recall and false-alarm rates per camera-hour
- Deploy and monitor models in production, diagnose drift and failure cases, and retrain models
- Work with product, solutions engineers and customers to turn requirements into measurable model specifications
- Write clean, tested and reviewed code and contribute to shared ML tooling
- Collaborate on data collection, processing and maintenance for model training and testing
- Integrate machine learning models into broader products and systems
- Conduct on-site travel and field testing for live customer deployments when needed
- Evaluate model performance using qualitative and quantitative methods and iterate to improve accuracy and efficiency
- Research and apply developments in machine learning and computer vision
- Troubleshoot complex computer vision system development and deployment issues
Requirements
What you’ll need- 3–5 years of industry experience building ML systems, with at least 2 years focused on computer vision
- BS or MS in Computer Science, Electrical Engineering, Math or a related field, or equivalent practical experience
- Strong Python and hands-on experience with PyTorch (or TensorFlow) for training and debugging deep learning models
- Shipped at least one computer vision model to production, and can explain the trade-offs made along the way
- Solid grasp of CNN and transformer architectures for detection, segmentation and classification
- Experience with OpenCV and image/video processing: decoding streams, preprocessing, augmentation
- Working knowledge of model optimization for inference (ONNX, TensorRT, quantization or similar)
- Comfortable with Docker, Git, Linux and training on GPUs in a cloud environment (AWS/Azure)
- Sound ML fundamentals: loss functions, overfitting, class imbalance, evaluation metrics and experiment design
- Clear written and verbal communication, including explaining model behavior to non-ML stakeholders
Benefits
Comp & perks- Health insurance
- Dental insurance
- Vision insurance
- 401K with 3% match
- Extensive PTO
- Life insurance policies included
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Object DetectionClassificationSegmentationTrackingAnomaly DetectionCNN ArchitecturesTransformer ArchitecturesModel Evaluation MetricsData Pipeline ManagementImage/Video Processing
Soft Skills
Clear CommunicationCollaborationProblem-Solving
Tools & Technologies
DockerGitLinuxAWSAzureOpenCVONNXTensorRTQuantizationSynthetic Data Generation
Industry Keywords
Deep LearningMachine Learning FundamentalsModel TrainingField TestingData Augmentation