Candidate Profile — Candidate #3
Position: Metropolis — .io - Senior Machine Learning Engineer, Computer Vision (Los Angeles, CA or Seattle, WA)
Candidate Location: Not specified
Experience: 8.5 years experience
Relevant Experience & Education Highlights
Brings deep expertise in computer vision evaluation, object detection performance analysis, and deep learning model development from research and applied roles, making a strong fit for advancing Metropolis's computer vision initiatives.
1. Design, develop, and deploy advanced computer vision models for real-world applications, including object detection, tracking, OCR, image search, and scene understanding
Tackled computer vision challenges through innovative research and library development.
- Developed DataEval library for computer vision testing and evaluation at a specialized AI research organization.
- Created performance bounds for object detection models serving multiple customer domains.
- Built decision boundary estimators to evaluate theoretical capabilities of machine learning models in computer vision contexts.
- Developed novel algorithms for dataset pruning and online dataset prioritization to enhance computer vision workflows.
2. Build and optimize deep learning models, ensuring high accuracy, performance, and scalability for deployment in production environments
Engineered sophisticated deep learning architectures for predictive modeling in research settings.
- Integrated transformers into neural network architecture for mechanistic deep learning model estimating somatic hypermutation rates at a leading biomedical research institute.
- Formulated deep learning model to estimate biological parameters governing immune cell diversity from immunologic data during doctoral research at a top-tier research university.
- Developed methods for posterior quantile estimation using recurrent neural networks as part of dissertation on neural networks for inference.
- Created framework employing reinforcement learning for optimizing Bayesian adaptive clinical trials.
3. Collaborate with cross-functional teams, including data engineers and software engineers to deliver end-to-end solutions
Contributed to interdisciplinary projects bridging machine learning with domain expertise.
- Collaborated with biologists to refine deep learning modeling framework during postdoctoral research.
- Worked within Modeling and Optimization team of data scientists and software engineers during internship at a major e-commerce technology company.
- Applied machine learning models for auditing delivery packages in production-like environments at a major technology company.
Requirements & Candidate Alignment
| Metropolis Requirement | Candidate Qualification |
|---|---|
| PhD in Computer Science, Engineering, or a related field, or equivalent work experience | PhD in Biostatistics from a top-tier research university |
| 5+ Years of hands-on experience in machine learning and computer vision, with a strong track record of deploying models into production | 8.5 years of hands-on machine learning experience, including computer vision research and applied scientist roles at major technology companies |
| Proficiency in Python and ML frameworks (PyTorch/TensorFlow/ONNX/TensorRT) | Proficiency in Python demonstrated across machine learning research, library development, and model building |
| Strong experience with model optimization (e.g., quantization, pruning) and deployment on various platforms (cloud, edge, or mobile) | Developed optimization techniques including dataset pruning algorithms and performance bounds for object detection models |
| Familiarity with cloud platforms (AWS, GCP, or Azure), containerization (Docker), and orchestration (ECS, Kubernetes) | Hands-on roles at leading cloud services provider with scalable ML model development experience |
| Proven experience in building and maintaining data pipelines (e.g., Airflow) | Built data evaluation libraries and prioritization algorithms for machine learning workflows |
| Strong understanding of the agile development process and CI/CD pipelines and tools (e.g., Github Actions, Jenkins) | Collaborated in team environments on ML modeling and optimization projects |
| Excellent communication skills, capable of presenting complex technical information clearly | Presented research through dissertation and cross-domain collaborations with biologists and engineers |
If you would like to discuss this candidate or other critical roles, here is a link to my calendar to schedule a call:
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