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Jobs(39,612)/Machine Learning Engineer Role(466)/Buzz Solutions (5) - Computer Vision & Machine Learning Engineer
Buzz Solutions

Buzz Solutions - Computer Vision & Machine Learning Engineer

Remote - USA2mo ago
RemoteMidNAArtificial IntelligenceNonprofitMachine Learning EngineerComputer Vision EngineerTransformersGitUnitDockerPandas

Requirements

• 2-4 years of industry experience in computer vision and machine learning • Object detection • Semantic segmentation • Image classification • Vision transformers and foundation models • Demonstrated ability to read ML research papers, extract key ideas, and implement them • Experience adapting published methods to specific use cases and validating against baselines • Experience selecting, fine-tuning, and adapting model architectures (CNNs, transformers, foundation models) for specific use cases • Ability to debug training instabilities and conduct systematic error analysis • PyTorch and Lightning • NumPy and pandas • Git version control • Unit and integration testing (Pytest) • CI/CD pipelines (GitHub Actions) • Experiment tracking and model versioning • Docker and reproducible environments • Python type hinting • Buzz Solutions does not provide Visa sponsorship for work authorizations in the United States at this time *

Responsibilities

• Stay current with ML/CV research, identify promising methods, and evaluate their applicability to our domain • Adapt and implement algorithms from papers, validating against baselines and benchmarking for production viability • Equipment defect detection • Thermal anomaly identification • Vegetation encroachment monitoring • Design and execute experiments with systematic hyperparameter tuning, ablation studies, and appropriate baselines • Perform structured error analysis: categorize failure modes (false positives, missed detections, localization errors, misclassifications) and break down performance by data slices (object size, occlusion, image quality) • Select and justify model architectures based on task requirements, latency, and accuracy tradeoffs • Design and implement data pipelines including ingestion, preprocessing, annotation workflows, and quality monitoring • Experiment tracking and model versioning (configurations, random seeds, dataset versions, environment specs, and model checkpoints) • Build model serving pipelines that meet latency and throughput requirements • Conduct thorough code reviews and write integration tests for ML pipelines • Communicate research findings, technical decisions, and model limitations clearly to stakeholders

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