Computer Vision & Machine Learning Engineer

Buzzsolutions; United States; On-site; USD 120,000 - 170,000; Full time

Job summary

Buzzsolutions seeks a Machine Learning Engineer to advance computer vision initiatives for power grid analysis. You will bridge cutting-edge research and production, turning papers into reliable deployed models for infrastructure monitoring.

You will join a team of ML engineers with autonomy to drive projects from problem framing to deployment and monitoring, delivering CV solutions tailored to client needs and performance requirements.

Qualifications

• 2-5 years of industry experience in computer vision and ML.
• Solid understanding of modern CV and deep neural networks, including object detection, segmentation, and transformers.
• Experience taking at least one ML model into production and maintaining it there.
• Experience selecting, fine-tuning, and adapting CNNs/transformers for specific use cases.
• 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 core ML stack (PyTorch, OpenCV, NumPy, Pandas, Scikit-Learn).

Responsibilities

• Own and deliver end-to-end computer vision projects focused on equipment defect detection, thermal anomaly identification, vegetation encroachment monitoring, and surveillance.
• Scope, plan, and execute projects from problem framing through production deployment and monitoring.
• Deliver client projects by translating requirements and raw data into working CV solutions.
• Collaborate with other engineers to deliver against shared milestones.
• Stay current with ML/CV research and evaluate applicability to our domain.
• Adapt algorithms from papers and validate baselines for production viability.
• Build production-grade Python libraries and data pipelines for the ML lifecycle.
• Own experiment tracking and model versioning, including configs and dataset versions.
• Design model serving pipelines meeting latency and throughput requirements.
• Conduct code reviews and write integration tests for ML pipelines.
• Share knowledge and uphold software quality standards within the team.

Skills

Computer vision

Machine learning

Python

PyTorch

OpenCV

NumPy

Pandas

Git

Docker

Model deployment

Team collaboration

Tools

PyTorch

OpenCV

NumPy

Pandas

Scikit-Learn

FastAPI

Pydantic

Docker

Git

pytest

Job description

Job Description

Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systemsAnalyze critical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network. We’re looking for a Machine Learning Engineer to advance our computer vision initiatives and help build our foundational model capabilities. You’ll bridge the gap between cutting-edge research and production systems, reading papers, adapting novel algorithms, and turning them into reliable, deployed models for power grid analysis. You’ll work within a team of experienced ML engineers, with the autonomy to drive your own projects and the support to keep growing.

Responsibilities

Project delivery

• Own and deliver end-to-end computer vision projects focused on:
  
  
  
  • Equipment defect detection
  
  
  • Thermal anomaly identification
  
  
  • Vegetation encroachment monitoring
  
  
  • Surveillance of closed areas for human and animal intrusion
  
  
  
  


• Scope, plan, and execute your own projects from problem framing through production deployment and monitoring.


• Deliver on client projects, translating client requirements and raw data into working computer vision solutions.


• Contribute to shared team projects, coordinating with other engineers to deliver against common milestones.

Research and experimentation

• Stay current with ML/CV research,identifypromising methods, and evaluate their applicability to our domain.


• Adapt and implement algorithms from papers,validatingagainst baselines and benchmarking for production viability.


• Bring the latest advances in deep learning and generative AI to bear on model training, accuracy, and reliability.


• Design and execute experiments with systematic hyperparameter tuning, ablation studies, andappropriate 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 accuracytradeoffs.

Engineering and production

• Develop production-grade Python libraries for the complete ML lifecycle.


• Design and implement data pipelines including ingestion, preprocessing, annotation workflows, and quality monitoring.


• Own 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.

Collaboration and craft

• Share knowledge with teammates and contribute to best practices for model development, evaluation, deployment, and monitoring.


• Advocate for and uphold software quality standards within the ML team.


• Communicate research findings, technical decisions, and model limitations clearly to stakeholders and clients.

Qualifications & Experience

• 2-5 years of industry experience in computer vision and machine learning.


• Solid understanding in modern computer vision and deep neural networks, including:
  
  
  
  • Object detection
  
  
  • Semantic segmentation
  
  
  • Image classification
  
  
  • Vision transformers and foundation models
  
  
  • Vision language models
  
  
  • Similarity search
  
  
  
  


• Experience taking at least one ML model into production andmaintainingit there.


• Experience selecting, fine-tuning, and adapting model architectures (CNNs, transformers, foundation models) for specific use cases.


• Demonstrated ability to read ML research papers, extract the key ideas, and implement them.


• Ability to debug training instabilities and conduct systematic error analysis.


• Proficiencyin Python and the core ML stack:
  
  
  
  • PyTorchand Lightning
  
  
  • OpenCV
  
  
  • NumPy and pandas
  
  
  • Scikit-Learn
  
  
  • FastAPI and Pydantic
  
  
  
  


• Strong software engineering practices, including:
  
  
  
  • Git version control
  
  
  • Unit and integration testing (Pytest)
  
  
  • CI/CD pipelines (GitHub Actions)
  
  
  • Docker and reproducible environments
  
  
  • Experiment tracking and model versioning
  
  
  • ML DevOps
  
  
  • Python type hinting
  
  
  
  


• Proven ability to own technical projects independently, from problem framing through production deployment.

Desired Additional Experience

• Multi-modal computer vision


• Custom object detection model development


• Generative models for data augmentation


• Extracting measurements from GIS and/or drone-metadata-enriched imagery


• Model quantization and latency optimization for edge deployment


• Systematic hyperparameter tuning at scale


• Energy, utilities, geospatial, or industrial inspection domains

Additional information:

• This position does not include sponsorship forUnited States work authorization.