The job, in the employer's words
About Obvio AI
Each year, more than 40,000 people in the U.S. leave home and never make it back due to traffic crashes. At Obvio, we believe these deaths are preventable. We deploy solar-powered, AI-assisted cameras to enforce traffic laws where pedestrians are most vulnerable, automating enforcement in ways that traditional systems cannot. Our approach has already led to a 50% reduction in reckless driving in early partner cities.
Founded by the team behind Motive's AI dashcam and backed by Bain Capital Ventures and Khosla Ventures, we are building the intelligence layer for safer streets globally.
About the Role
Obvio 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 $54M
Fast-moving startup environment with meaningful ownership
Competitive compensation and early-stage equity
Obvio is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Obvio considers qualified applicants with criminal histories, consistent with applicable federal, state, and local law. Obvio is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.
Requirements mentioned
- 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.
No Mondays shows this listing as the employer published it. Check the details on the employer's site before you apply.