Full Stack Engineer - Traffic Studies

Obvio•San Carlos, CA

4d•Onsite

About The Position

Obvio AI is hiring a Full-Stack Engineer for its traffic studies team. The team conducts short camera- and radar-based studies to measure traffic behavior at specific city locations before enforcement programs begin. The engineer will own the software journey from field deployment through publication of city-facing reports, making routine studies repeatable and largely automated while enabling new cities, sensors, metrics, and report formats through configuration and small extensions. The role connects computer-vision engineers, annotation reviewers, field operations, and go-to-market teams. It includes backend services and data pipelines, internal tools and APIs, cloud and ML workload orchestration, database design, production monitoring, and accurate, auditable reporting. The position is based in Islamabad or Lahore and requires working from the office.

Requirements

4+ years of professional software engineering experience with meaningful full-stack ownership, including backend services and data pipelines as well as internal tools and front ends.
Experience building and operating services on a major cloud platform; AWS is preferred, including ECS/EC2, S3, and IAM.
Solid database skills, including schema design, indexing, and query performance on growing datasets using Postgres or a similar database.
Experience building internal tools or dashboards using a low-code platform such as Retool or a modern web framework.
Experience operating production systems, including monitoring, alerting, incident response, and post-incident fixes.
Experience orchestrating batch or compute-heavy workloads, such as job queues, workers, GPU/ML servers, or similar systems.
Based in Islamabad or Lahore and able to work from the office.
End-to-end ownership, pragmatic execution, an automation mindset, rigor around data traceability and auditability, clear communication, and comfort with ambiguity.

Nice To Haves

Experience with pipelines consuming computer-vision or sensor outputs such as detections, tracks, or radar, or with video and imagery at volume.
Experience with data-quality, annotation, or audit workflows.
Familiarity with IoT or edge devices and operational concerns such as timestamps, connectivity, and uploads.
Startup or small-team experience with end-to-end system ownership.

Responsibilities

Own the traffic-study backend end to end, including sensor ingestion, vision-pipeline orchestration on ML servers and cloud compute, event extraction, annotation hand-off, statistics, and publishing to the city-facing portal.
Automate routine study steps such as statistics entry, reprocessing, and report assembly, making them repeatable and auditable with minimal engineer involvement.
Design systems that accommodate new sensors, metrics, report formats, and tighter timelines through configuration and small extensions rather than rewrites.
Design and evolve data models, including schema design, indexing, and query performance for growing Postgres datasets.
Maintain monitoring and alerting for vision and event-extraction services, detect stuck or failed studies, and drive fixes to root cause.
Implement and version counting and violation logic from written specifications, ensuring reported figures can be traced to raw data and the logic used to produce them.
Coordinate with annotation teams on intake, view-quality gates, and delivery dates; with field operations on deployment timing and data hand-off; and with go-to-market on capacity and lead times.
Provide APIs and data endpoints for internal annotation and operations tools, including presentation and review managers, calibration tools, audit dashboards, and capacity and status views.