Machine Learning Engineer

Be an Early Applicant Remote Hiring Remotely in US Mid level Remote Hiring Remotely in US Mid level Build and deploy computer vision and machine learning solutions for power grid infrastructure, including defect detection, thermal anomalies, vegetation monitoring, and intrusion surveillance. Responsibilities span research implementation, model experimentation, error analysis, data pipelines, experiment tracking, model serving, testing, monitoring, and client delivery. The role requires productionizing models, adapting research papers, optimizing architectures, and independently owning projects from problem definition through deployment. The summary above was generated by AI, Buzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systems analyze 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., 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., Own end-to-end computer vision and machine learning projects for power grid infrastructure, from problem framing and research experimentation through production deployment and monitoring. Develop detection, segmentation, classification, anomaly detection, and foundation-model solutions; build data pipelines, serving systems, experiment tracking, and model versioning. Conduct error analysis, benchmarking, tuning, code reviews, and testing while translating client requirements into reliable production models and communicating technical decisions and limitations. Top Skills: Ci/CdCnnsDockerDrone MetadataFastapiFoundation ModelsGisGitGithub ActionsLightningMl DevopsModel QuantizationNumpyOpencvPandasPydanticPytestPythonPython Type HintingPyTorchScikit-LearnTransformersVision Language Models Entefy

Machine Learning Engineer

28 Days Ago Remote USA Mid level Mid level Artificial Intelligence * Big Data * Internet of Things * Software As a Machine Learning Engineer in Computer Vision, you’ll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools. Top Skills: CC++CaffeFfmpegGraphicsmagickJavaKerasMllibOpencvPil/PillowPythonSimplecvTensorFlowTheanoTorch, Lowe’s, Coordinates installation projects from initial customer and provider contact through scheduling, product delivery, compliance documentation, issue resolution, and post-completion work orders. Communicates with customers, service providers, stores, and vendors through inbound and outbound calls, documents interactions in company systems, ensures SLA adherence, assesses costs and chargebacks, and supports consistent customer service and process improvement in a fast-paced environment. Top Skills: Installation Management SystemMyredvestSalesforce

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Key Facts About Colorado Tech

• Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
• Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
• Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
• Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
• Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
• Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute

• 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 and maintaining it 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.

• 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

• 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