Senior Computer Vision & Machine Learning Engineer
TypeFull-time job
LocationUnited States
Posted1 hour ago
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, identify promising methods, and evaluate their applicability to our domain.
Adapt and implement algorithms from papers, validating against 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, and appropriate 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 accuracy tradeoffs.
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.
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
Similarity search
Proven track record of deploying and maintaining ML 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.
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 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
ML deployment on edge devices
Extracting measurements from GIS and/or drone metadata enriched imagery
Model quantization
Systematic hyperparameter tuning
Additional information:
This position does not include sponsorship for United States work authorization.
Originally posted on Himalayas
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, identify promising methods, and evaluate their applicability to our domain.
Adapt and implement algorithms from papers, validating against 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, and appropriate 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 accuracy tradeoffs.
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.
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
Similarity search
Proven track record of deploying and maintaining ML 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.
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 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
ML deployment on edge devices
Extracting measurements from GIS and/or drone metadata enriched imagery
Model quantization
Systematic hyperparameter tuning
Additional information:
This position does not include sponsorship for United States work authorization.
Originally posted on Himalayas
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