Job DescriptionWe'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.
ResponsibilitiesProject 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- 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
Additional information:- This position does not include sponsorship for United States work authorization.
