СШАFull Stack Engineer - Traffic Studies
4 дн. назад
СШАOnsite
software development
Develop software to automate and scale traffic study data processing and reporting for safer streets.
О компании
- Each year, more than 40,000 people in the U.S. leave home and never make it back due to traffic crashes. At , 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.
Обязанности
- 's traffic studies team runs short, camera- and radar-based data studies that measure speeding, red-light, stop-sign and crosswalk behavior at specific locations in a city before any enforcement program starts. Cities rely on these results, so accuracy and timely delivery matter.
- Today the pipeline works, but it scales with people rather than software. We are hiring a Full-Stack Engineer to change that. You will own the software that takes a study from field deployment to a published report, make the routine path repeatable and largely automated, and make ad-hoc requests (a new city, a new sensor, a new metric, a tighter timeline) something the system takes on through configuration and small extensions rather than one-off engineering effort.
- You will work alongside computer-vision engineers who hand you detectors and trackers, an annotation team that reviews and audits the results, field operations who deploy the hardware, and a go-to-market team that sets delivery dates with cities. Your job is the connective tissue between them.
- Own the traffic-study backend end to end: ingestion from multiple sensor types, vision-pipeline orchestration on our ML servers and cloud compute, event extraction, annotation hand-off, statistics, and publishing to the city-facing portal (e.g., Python services on AWS ECS/EC2, S3 event data, Postgres).
- Scale the routine path: turn manual steps (stats entry, reprocessing, report assembly) into automated, auditable ones so a study runs the same way every time with minimal engineer involvement.
- Handle ad-hoc requests: new sensors (each with its own data layout and quirks), new metrics, new report formats and shorter timelines. Design the system so these become configuration and small extensions rather than rewrites.
- Design and evolve the data model: schema design, indexing, and query performance on growing Postgres datasets that back both the pipeline and the internal tools built on top of it.
- Own pipeline health: maintain monitoring and alerting for the vision and event-extraction services, catch stuck or failed studies early, and drive fixes to root cause.
- Make the numbers defensible: implement counting and violation logic from written specifications, version it, and make every reported figure traceable to the raw data and the logic that produced it.
- Coordinate across teams: work with the annotation team on intake, view-quality gates and delivery dates, and with field operations on deployment timing and data hand-off; give the go-to-market team realistic capacity and lead-time answers.
- Support internal tooling as a backend: expose the APIs and data endpoints that the annotation and operations teams' tools (e.g., built in Retool) consume — presentation and review managers, calibration tools, audit dashboards, capacity and status views.
Требования
- An end-to-end owner: you prefer to be accountable for a whole system and its output rather than a single layer of it.
- Pragmatic: you balance shipping fixes quickly with leaving the codebase in better shape than you found it.
- Automation-minded: when you see a recurring manual step, your instinct is to design it out of the process.
- Rigorous with data: you understand that an inaccurate count delivered to a city is a credibility problem, and you build for traceability and auditability from the start.
- A clear communicator: you write things down, make requirements explicit before implementing them, and work well across engineering, annotation, operations and go-to-market teams.
- Comfortable with ambiguity: priorities shift with customer needs, and you can re-plan without losing momentum.
- 4+ years of professional software engineering experience with meaningful full-stack ownership: backend services and data pipelines, plus the internal tools and front ends people use to operate them.
- Experience building and operating services on a major cloud (AWS preferred: ECS/EC2, S3, IAM).
- Solid database skills: schema design, indexing and query performance on growing datasets (Postgres or similar).
- Experience building internal tools or dashboards, whether on a low-code platform such as Retool or with a modern web framework.
- Experience running production systems: monitoring, alerting, incident response and post-incident fixes.
- Experience orchestrating batch or compute-heavy workloads (job queues, workers, GPU/ML servers, or similar).
- Based in Islamabad or Lahore and able to work from the office.
Будет плюсом
- Experience with pipelines that consume computer-vision or sensor outputs (detections, tracks, radar), or with video and imagery at volume.
- Experience with data-quality, annotation or audit workflows.
- Familiarity with IoT or edge devices and their operational quirks (timestamps, connectivity, uploads).
- Startup or small-team experience where you owned a system end to end.
Другое
- 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