Obvio AI
AI
SeniorAIInfrastructureEngineer-ComputerVision
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior AI Infrastructure Engineer - Computer Vision at Obvio AI. Skills: AI Infrastructure, Computer Vision, ML Pipelines, Orchestration, Cloud Infrastructure, ML Systems. Build the orchestration layer. Design and implement a scalable workflow system to ingest, route, and process incoming events”
What You'll Achieve.
50% reduction in reckless driving in early partner cities
Industry & Context.
Handle failures gracefully at high throughput; Optimize for GPU utilization and throughput; Ensure new model versions can be promoted and rolled back without pipeline downtime
What They're Looking For.
Must Have
6+ years building and operating production backend or data-intensive systems at scale, Meaningful experience working on ML-heavy pipelines, Owned something through its full lifecycle — design, deployment, scaling, and on-call, Experience in a context where ML inference was a first-class part of the system, Hands-on orchestration experience, Used a workflow orchestration tool to build production pipelines, Comfortable with the building blocks — compute, queues, storage, networking, Enough ML systems fluency to orchestrate them well, Built or operated pipelines where ML inference is a core stage, Understand what ML inference workloads need — throughput constraints, GPU economics, model versioning, and keeping model performance visible in production, Pragmatic decision-maker
Nice to Have
Experience with CV or video pipelines
What You'll Do.
Build the orchestration layer
Design and implement a scalable workflow system to ingest
and process incoming events
Define the stages of the pipeline — ingestion
and delivery — and build something that handles failures gracefully at high throughput
Scale the inference fleet
Build the compute layer that parallelizes processing across the event backlog and handles burst capacity as our camera fleet grows
Design the worker pool
and autoscaling strategy for GPU-bound workloads on ECS
Design the data plumbing
Own the path from edge device to pipeline output — storage
and the triggers that drive processing
Build something that is observable
and auditable end-to-end
Build the model serving and lifecycle layer
Stand up the infrastructure that loads versioned CV models and handles inference reliably
Optimize for GPU utilization and throughput where it matters — dynamic batching
and model optimizations like quantization or TensorRT/ONNX
Ensure new model versions can be promoted and rolled back without pipeline downtime
Set the engineering standard
Write the playbooks — runbooks
deployment procedures
testing standards — that the team builds on as we grow
How You'll Work.
Team & Collaboration
Set the engineering standard; Write the playbooks — runbooks, deployment procedures, testing standards — that the team builds on as we grow
Full Job Description
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. WHAT YOU'LL DO Build the orchestration layer. Design and implement a scalable workflow system to ingest, route, and process incoming events. Define the stages of the pipeline — ingestion, preprocessing, inference, validation, and delivery — and build something that handles failures gracefully at high throughput. Scale the inference fleet. Build the compute layer that parallelizes processing across the event backlog and handles burst capacity as our camera fleet grows. Design the worker pool, queueing, and autoscaling strategy for GPU-bound workloads on ECS. Design the data plumbing. Own the path from edge device to pipeline output — storage, metadata, and the triggers that drive processing. Build something that is observable, debuggable, and auditable end-to-end. Build the model serving and lifecycle layer. Stand up the infrastructure that loads versioned CV models and handles inference reliably. Optimize for GPU utilization and throughput where it matters — dynamic batching, multi-model serving, and model optimizations like quantization or TensorRT/ONNX. Ensure new model versions can be promoted and rolled back without pipeline downtime. Set the engineering standard. This is an early hire. You'll write the playbooks — runbooks, deployment procedures, testing standards — that the team builds on as we grow. WHAT WE'RE LOOKING FOR Depth in backend systems. 6+ years buil
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