Candidate Profile — Candidate #1
Position: Metropolis — .io - Senior Machine Learning Engineer, Computer Vision (Los Angeles, CA or Seattle, WA)
Candidate Location: San Francisco Bay Area
Experience: 2.3 years experience
Relevant Experience & Education Highlights
Demonstrates strong alignment with computer vision model development, optimization, deployment, and multi-modal integration through hands-on projects in detection, segmentation, and real-time robotics applications.
1. Design, develop, and deploy advanced computer vision models for real-world applications, including object detection, tracking, OCR, image search, and scene understanding
Applied computer vision expertise to detection, segmentation, and pose estimation tasks in research and industry settings.
- Fine-tuned vision-language models including ViT and CLIP using PEFT and LoRA for casualty detection and classification in a major research challenge.
- Developed Swin Transformer-based UNet with attention mechanisms for state-of-the-art scene segmentation.
- Integrated NVIDIA FoundationPose for 6D pose estimation on robotic arms with ROS2, ensuring real-time synchronization of perception and depth sensors.
- Optimized encoder diagnostics using ROS2 and UR10 robotic arms, improving accuracy in vision engineering tasks.
2. Build and optimize deep learning models, ensuring high accuracy, performance, and scalability for deployment in production environments
Optimized deep learning models for edge deployment and multi-GPU training, focusing on real-time inference.
- Deployed scalable models using PyTorch, TensorRT, and ONNX for real-time inference on autonomous robots.
- Optimized large-scale model training on multi-GPU clusters for multimodal AI deployment.
- Implemented 3D UNet for medical imaging segmentation with CUDA management.
- Led development of Image to 3D Foundation Models and Image-to-Image Flow Matching Models for photorealistic content generation at a major retail company.
3. Explore and integrate multi-modal approaches, leveraging visual, textual, and other data modalities for robust solutions
Integrated visual and language models for enhanced multimodal capabilities in generation and perception.
- Fine-tuned VLMs like ViT and CLIP for multimodal AI on Spot robots, reducing false positives in detection tasks.
- Scaled GenAI workflows supporting multimodal content generation across multiple brands at a major retail company.
- Built autonomous perception pipelines combining vision models with path-planning algorithms like A* and RRT* using ROS2 and Nav2.
Requirements & Candidate Alignment
| Metropolis Requirement | Candidate Qualification |
|---|---|
| PhD in Computer Science, Engineering, or a related field, or equivalent work experience | MEng in Robotics 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 | 2.3 years of hands-on experience across ML research engineering, applied research, and university roles with model deployments |
| Proficiency in Python and ML frameworks (PyTorch/TensorFlow/ONNX/TensorRT) | Proficiency in PyTorch, ONNX, and TensorRT for model development and deployment |
| Strong experience with model optimization (e.g., quantization, pruning) and deployment on various platforms (cloud, edge, or mobile) | Model optimization and edge deployment using TensorRT, ONNX, and multi-GPU clusters for real-time robotics inference |
| Familiarity with cloud platforms (AWS, GCP, or Azure), containerization (Docker), and orchestration (ECS, Kubernetes) | Containerization (Docker) and orchestration (Kubernetes) experience supporting scalable ML workflows |
| Proven experience in building and maintaining data pipelines (e.g., Airflow) | Built perception and training pipelines integrating ROS2, Nav2, and multi-GPU processing for autonomous systems |
| Strong understanding of the agile development process and CI/CD pipelines and tools (e.g., Github Actions, Jenkins) | Agile development alignment through iterative research and deployment in fast-paced startup and research environments |
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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