The role involves developing, adapting, and implementing machine learning algorithms for computer vision tasks in power grid analysis. Responsibilities include experimentation, error analysis, and project ownership.
About Us
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.
Job Description
We're looking for a Machine Learning Engineer to join our computer vision team 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.
Responsibilities
- 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
- Own and deliver end-to-end computer vision projects focused on:
- Equipment defect detection
- Thermal anomaly identification
- Vegetation encroachment monitoring
- 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
- Design and implement data pipelines including ingestion, preprocessing, annotation workflows, and quality monitoring
- 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
- Communicate research findings, technical decisions, and model limitations clearly to stakeholders
Qualifications & Experience
- 2-4 years of industry experience in computer vision and machine learning
- Solid understanding of modern computer vision and deep neural networks including:
- Object detection
- Semantic segmentation
- Image classification
- Vision transformers and foundation models
- Demonstrated ability to read ML research papers, extract key ideas, and implement them
- Experience adapting published methods to specific use cases and validating against baselines
- Experience selecting, fine-tuning, and adapting model architectures (CNNs, transformers, foundation models) for specific use cases
- Ability to debug training instabilities and conduct systematic error analysis
- Proficiency in Python and core ML libraries:
- PyTorch and Lightning
- OpenCV
- NumPy and pandas
- Scikit-Learn
- Strong software engineering practices:
- Git version control
- Unit and integration testing (Pytest)
- CI/CD pipelines (GitHub Actions)
- Experiment tracking and model versioning
- Docker and reproducible environments
- Python type hinting
* Buzz Solutions does not provide Visa sponsorship for work authorizations in the United States at this time *
What you need to know about the Chicago Tech Scene
With vibrant neighborhoods, great food and more affordable housing than either coast, Chicago might be the most liveable major tech hub. It is the birthplace of modern commodities and futures trading, a national hub for logistics and commerce, and home to the American Medical Association and the American Bar Association. This diverse blend of industry influences has helped Chicago emerge as a major player in verticals like fintech, biotechnology, legal tech, e-commerce and logistics technology. It’s also a major hiring center for tech companies on both coasts.
Key Facts About Chicago Tech
- Number of Tech Workers: 245,800; 5.2% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: McDonald’s, John Deere, Boeing, Morningstar
- Key Industries: Artificial intelligence, biotechnology, fintech, software, logistics technology
- Funding Landscape: $2.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Pritzker Group Venture Capital, Arch Venture Partners, MATH Venture Partners, Jump Capital, Hyde Park Venture Partners
- Research Centers and Universities: Northwestern University, University of Chicago, University of Illinois Urbana-Champaign, Illinois Institute of Technology, Argonne National Laboratory, Fermi National Accelerator Laboratory
