Senior Computer Vision, Machine Learning Engineer

🔥 13 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 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.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

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 focused on 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 other engineers to deliver common milestones • Stay current with ML/CV research, identify promising methods, and evaluate applicability to the domain • Adapt and implement algorithms from research papers, validating against baselines and benchmarking for production viability • Apply advances in deep learning and generative AI to model training, accuracy, and reliability • Design and execute 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 and contribute to best practices for model development, evaluation, deployment, and monitoring • Uphold software quality standards within the ML team • Communicate research findings, technical decisions, and model limitations to stakeholders and clients

🎯 Requirements

• 5–10 years of industry experience in computer vision and machine learning • Deep expertise in 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 • Proven track record of deploying and maintaining ML models in production • Experience selecting, fine-tuning, and adapting CNN, transformer, and foundation model architectures for specific use cases • Demonstrated 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 and Lightning, OpenCV, NumPy and 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 and 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