Computer Vision & Machine Learning Engineer

Buzz Solutions; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Buzz Solutions is seeking a Machine Learning Engineer to advance computer vision initiatives and build foundational model capabilities. You will bridge cutting-edge research and production systems, reading papers, adapting algorithms, and turning them into deployed models for power grid analysis.

You’ll work with a team of ML engineers, with autonomy to drive projects and support to grow, in a role focused on end-to-end delivery, experimentation, and scalable ML tooling.

Qualifications

• 2–5 years of industry experience in computer vision and ML.
• Production experience: at least one model in production and maintenance.
• Strong Python skills and core ML stack proficiency.
• Experience with CV architectures (CNNs, transformers) and evaluation.

Responsibilities

• Own end-to-end computer vision projects from framing to production deployment.
• Develop production-grade libraries and data pipelines for ML lifecycles.
• Experiment tracking, model versioning and robust testing practices.
• Collaborate with ML engineers and stakeholders to meet client needs.
• Read research, implement ideas, and validate against baselines.

Skills

Computer vision

Machine learning

Python

PyTorch

OpenCV

NumPy

Pandas

Scikit-Learn

Lightning

Vision transformers

Foundation models

Git

CI/CD

Docker

Experiment tracking

Model versioning

PyTest

FastAPI

Pydantic

Tools

PyTorch

OpenCV

Lightning

FastAPI

Pydantic

Docker

Git

CI/CD

Job description

Job Description

Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systemsanalyzecritical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network.

We’relooking for a Machine Learning Engineer to advance our computer vision initiatives and help build our foundational model capabilities.You’llbridge the gap betweencutting-edgeresearch and production systems,reading papers, adapting novel algorithms, and turning them into reliable, deployed models for power grid analysis.You’llwork 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 inmodern 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.