Computer Vision & Machine Learning Engineer

🔥 15 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟢 Junior

🟡 Mid-level

🤖 Machine Learning Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 24%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Buzz Solutions

WebsiteLinkedInAll Job Openings

11 - 50 employees

⚡ Energy

🤖 Artificial Intelligence

🔐 Security

Energy • Artificial Intelligence • Security

Buzz Solutions is a company specializing in visual intelligence solutions for the energy infrastructure sector. Their platform leverages artificial intelligence to inspect, maintain, and secure energy assets such as transmission lines, distribution grids, and substations. The company aims to modernize energy infrastructures by providing actionable intelligence to utility teams, enhancing operations, improving security, and optimizing project management. Buzz Solutions enables condition-based maintenance, detecting anomalies and defects quickly to prevent failures and manage vegetation effectively, especially during adverse weather conditions. The company collaborates with large utility organizations, offering technology that improves inspection efficiency, reduces maintenance costs, and enhances the reliability of energy supply.

📋 Description

• Own and deliver end-to-end computer vision projects for equipment defect detection, thermal anomaly identification, vegetation encroachment monitoring, and surveillance of closed areas for human and animal intrusion • Scope, plan, and execute projects from problem framing through production deployment and monitoring • Deliver client projects by translating client requirements and raw data into working computer vision solutions • Contribute to shared team projects and coordinate with engineers to deliver common milestones • Stay current with ML/CV research and evaluate promising methods for the power-grid domain • Adapt and implement algorithms from research papers, validate against baselines, and benchmark production viability • Apply advances in deep learning and generative AI to model training, accuracy, and reliability • Design experiments with hyperparameter tuning, ablation studies, and appropriate baselines • Perform structured error analysis across failure modes and data slices • Select and justify model architectures based on task requirements, latency, and accuracy tradeoffs • Develop production-grade Python libraries for the complete ML lifecycle • Design and implement data pipelines for ingestion, preprocessing, annotation workflows, and quality monitoring • Own experiment tracking and model versioning • Build model-serving pipelines meeting latency and throughput requirements • Conduct code reviews and write integration tests for ML pipelines • Share knowledge, uphold ML software-quality standards, and communicate research findings, technical decisions, and model limitations to stakeholders and clients

🎯 Requirements

• 2-5 years of industry experience in computer vision and machine learning • Solid understanding of modern computer vision and deep neural networks, including object detection, semantic segmentation, image classification, vision transformers and foundation models, vision language models, and similarity search • Experience taking at least one ML model into production and maintaining it there • Experience selecting, fine-tuning, and adapting CNN, transformer, and foundation model architectures for specific use cases • Proven ability to read ML research papers, extract key ideas, and implement them • Ability to debug training instabilities and conduct systematic error analysis • Proficiency in Python and the core ML stack: PyTorch, Lightning, OpenCV, NumPy, pandas, Scikit-Learn, FastAPI, and Pydantic • Strong software engineering practices, including Git, unit and integration testing with Pytest, CI/CD pipelines with GitHub Actions, Docker, reproducible environments, experiment tracking, model versioning, ML DevOps, and Python type hinting • Proven ability to own technical projects independently from problem framing through production deployment • United States work authorization; the position does not include sponsorship