7+ years in embedded or edge systems with a proven track record of shipping production hardware/software products.
Strong C/C++ programming skills with extensive experience in embedded Linux and system-level debugging.
Hands-on experience deploying computer vision or machine learning inference on edge devices.
Familiarity with camera and image pipelines like V4L2, GStreamer, and synchronization techniques.
Experience optimizing models with production runtimes or accelerators such as TensorRT or TFLite.
Ability to balance accuracy, performance, reliability, and operational costs in production environments.
Demonstrated technical leadership in cross-functional projects with effective communication.
Responsibilities
Own the production embedded vision stack, covering all aspects from image capture to cloud interfaces.
Design software for real-world operations, focusing on resource constraints and fault recovery.
Build observability tools for deployed devices to diagnose field issues effectively.
Lead safe fleet releases with versioning and over-the-air update strategies.
Drive architecture and technical decisions across various system boundaries.
Own production issues by identifying root causes and implementing platform improvements.
Mentor engineers and establish standards for performance and maintainability.
Benefits
Opportunity to own systems from prototype to production fleet operation.
Work with a small, experienced team where senior engineers have significant technical influence.
Contribute to edge AI solutions that enhance road safety.
Fast-paced startup environment with meaningful ownership and impact.
Competitive compensation package and early-stage equity options.
Full Job Description
About the Role
Obvio deploys AI-assisted cameras to make streets safer. We're hiring a Senior Embedded Engineer to own the software that turns cameras, compute, and vision models into reliable products in the field.
You will be the senior technical owner for production vision systems on the edge-from image capture and model execution through device services, fleet observability, and safe rollout. This is a hands-on individual-contributor role: you'll write and review code, debug systems in the field, make architecture and trade-off decisions, and raise the engineering bar across the team. What You'll Do
Own the production embedded vision stack, including camera capture, media pipelines, model inference, tracking, pre- and post-processing, device services, and cloud interfaces.
Design software for real-world operation: constrained resources, long uptimes, fault recovery, data integrity, and remote diagnostics.
Build observability for deployed devices-metrics, logs, traces, crash data, health signals, and tools that make field issues diagnosable.
Lead safe fleet releases with versioning, staged rollout, compatibility checks, rollback, and over-the-air update strategies.
Drive architecture and technical decisions across embedded Linux, hardware, vision, backend, and product boundaries.
Own problems through production: respond to incidents, find root causes, and turn field learning into durable platform improvements.
Mentor engineers, lead design & architecture decisions, and establish practical standards for performance, reliability, testing, and maintainability.
What We're Looking For
7+ years building embedded or edge systems, with a track record of shipping and operating production hardware/software products.
Strong C/C++ skills, plus deep experience with embedded Linux, concurrency, IPC, networking, storage, and system-level debugging.
Hands-on experience deploying computer-vision or machine-learning inference on edge devices, not only training models or running cloud inference.
Experience with camera and image pipelines such as V4L2, GStreamer, codecs, synchronization, calibration, or equivalent systems.
Experience optimizing models with one or more production runtimes or accelerators such as TensorRT, SNPE, OpenVINO, or TFLite.
Production judgment: you can balance accuracy, performance, reliability, security, development speed, and operational cost.
Demonstrated technical leadership across ambiguous, cross-functional work, with clear communication and strong ownership.
Bonus Points
Experience with Qualcomm SNPE or other GPU/NPU-based edge platforms.
Experience with multi-camera systems, sensor fusion, real-time perception, or video analytics.
Experience with secure boot, device identity, signed updates, or hardening connected devices.
Experience operating outdoor, automotive, robotics, IoT, or other ruggedized products at fleet scale.
Why This Role
Own systems from prototype through production fleet operation.
Work with a small, experienced team where senior engineers have real technical influence.
Build edge AI that directly improves road safety.
Why Obvio
Your work will help save lives and improve road safety
Series A of $54M
Fast-moving startup environment with meaningful ownership
