About the RoleObvio deploys AI-assisted cameras to make streets safer. We're hiring a Senior Computer Vision Engineer to build the models and ML systems behind reliable detection and tracking in real-world traffic environments.
You will own the full model-development loop: data strategy, training, experimentation, evaluation, field validation, and continuous improvement. This is a hands-on individual-contributor role for someone who combines strong applied-ML judgment with the engineering discipline to make experiments reproducible, measurable, and fast.
What You'll Do- Develop and improve object-detection and multi-object-tracking models for vehicles, pedestrians, and other road users across challenging real-world conditions.
- Own data strategy for model quality: sampling, labeling, dataset versioning, hard-negative mining, class imbalance, edge cases, and feedback from deployed systems.
- Design rigorous experiments and ablations; distinguish real improvements from overfitting, leakage, noisy labels, or gains that do not survive field deployment.
- Build and evolve reproducible training pipelines with experiment tracking, configuration management, artifact lineage, model registries, metric dashboards, and automated hyperparameter search.
- Define evaluation that reflects product behavior-not only aggregate metrics-including precision/recall trade-offs, class and scenario slices, calibration and tracking quality.
- Partner with embedded engineers to optimize models for edge deployment while balancing accuracy, latency, memory & power constraints.
- Set technical direction, lead design reviews, mentor engineers, and raise standards for ML rigor, reproducibility, and production readiness.
What We're Looking For- 7+ years in machine learning or computer vision, with a track record of shipping and improving production models.
- Deep hands-on experience with object detection and multi-object tracking, including modern architectures, data augmentation and evaluation methods.
- Strong understanding of class imbalance, overfitting, dataset leakage, label noise, domain shift, model calibration, and statistically sound validation.
- Experience building training infrastructure or platforms that support reproducible experiments, distributed training, hyperparameter search, metric comparison, and model lineage.
- Strong Python and PyTorch skills, plus practical experience with large image/video datasets.
- Experience validating models on deployed or field-collected data and owning the loop from failure discovery through retraining and verified improvement.
- Demonstrated technical leadership across ambiguous, cross-functional work, with clear communication and strong ownership.
Bonus Points- Experience with traffic, automotive, robotics, surveillance, or other video-analytics domains.
- Experience with re-identification, trajectory modeling, occlusion handling, camera calibration, or multi-camera tracking.
- Experience with active learning, weak supervision, synthetic data, automated labeling, or dataset-quality tooling.
- Experience optimizing and deploying vision models on NVIDIA Jetson, Qualcomm Snapdragon, or other edge accelerators.
Why This Role- Build computer vision that directly improves road safety.
- Own the complete path from data and experiments to validated performance in the field.
- Work with a small, experienced team where senior engineers have real technical influence.
Why Obvio- Your work will help save lives and improve road safety
- Series A of $22M led by Bain Capital
- Fast-moving startup environment with meaningful ownership
- Competitive compensation and early-stage equity
