Senior Computer Vision & Machine Learning Engineer

Buzz Solutions; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Buzz Solutions seeks an experienced Machine Learning Engineer to advance computer vision initiatives and build foundational model capabilities for power grid analysis. You’ll bridge cutting-edge research and production systems, turning papers into deployed models with strong autonomy.

You will lead end-to-end CV projects, adapt latest DL techniques, and contribute to data pipelines, tooling, and model serving. Expect collaboration with senior engineers and a fast-moving environment.

Qualifications

• 5–10 years of industry experience in computer vision and machine learning.
• Deep expertise in modern computer vision and deep neural networks: object detection, semantic segmentation, image classification, vision transformers and foundation models.
• Proven track record of deploying and maintaining ML models in production.
• Experience selecting, fine-tuning, and adapting model architectures for specific use cases.
• Ability to read ML research papers, extract key ideas, and implement them.

Responsibilities

• Own and deliver end-to-end computer vision projects from problem framing through production deployment and monitoring.
• Stay current with ML/CV research and evaluate applicability to our domain; adapt algorithms and validate against baselines.
• Develop production-grade Python libraries and data pipelines for the ML lifecycle; manage experiment tracking and model versioning.
• Build model serving pipelines that meet latency and throughput requirements; write tests and perform code reviews.
• Communicate research findings and technical decisions clearly to stakeholders and clients.

Skills

Python proficiency

Deep learning

Computer vision

Model deployment

Experimentation

Tools

PyTorch

OpenCV

NumPy

pandas

Scikit-Learn

FastAPI

Pydantic

Git

Docker

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.You’lloperatewith a high degree of autonomy.

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

• 5–10 years of industry experience in computer vision and machine learning.
• Deepexpertiseinmodern computer vision and deep neural networks, including:
• Object detection
• Semantic segmentation
• Image classification
• Vision transformers and foundation models
• Vision language models
• Similarity search
• Proventrack recordof deploying andmaintainingML models in production.
• 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
• FastAPIandPydantic
• 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.

DesiredAdditionalExperience

• Multi-modalcomputervision
• Customobjectdetectionmodeldevelopment
• Generativemodelsfordataaugmentation
• MLdeploymentonedgedevices
• ExtractingmeasurementsfromGISand/ordronemetadataenrichedimagery
• Modelquantization
• Systematichyperparametertuning

Additional information:

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