Embedded Vision Engineer job description template
An embedded vision engineer gets computer vision running on constrained hardware — cameras, sensors, handsets and edge devices where memory, power and thermal budgets are tight. The role combines model optimisation with low-level systems work and often touches the hardware directly.
Before you post
Lead with the hardware. This is the one role where naming the target platform in the job title — "Embedded Vision Engineer (Jetson)" — measurably improves applicant quality, because the pool self-sorts by toolchain. Be honest about power and thermal constraints; candidates who enjoy this work find hard constraints attractive rather than off-putting.
The template
About the role
We are looking for an embedded vision engineer to bring computer vision to [device]. You will take trained models and make them run within the memory, latency and power budget of [target hardware].
What you will do
- Optimise and deploy vision models onto [target hardware]
- Apply quantisation, pruning and compression while protecting accuracy
- Profile and improve end-to-end pipeline performance on device
- Integrate with camera interfaces and the image pipeline
- Work with the modelling team on architectures that suit the hardware
What we are looking for
- Strong C and C++
- Experience deploying neural networks on embedded or mobile hardware
- Familiarity with at least one inference runtime (TensorRT, TFLite, ONNX Runtime, OpenVINO)
- Practical profiling and optimisation experience on target devices
Keep this list to three to five items. Long mandatory lists disproportionately deter the candidates you most want.
Nice to have
- Experience with [your specific SoC or platform]
- Camera and ISP pipeline knowledge
- Quantisation-aware training
- DSP, NPU or FPGA acceleration experience
Plain text version
Select all and paste into your ATS, then replace everything in brackets.
EMBEDDED VISION ENGINEER ABOUT THE ROLE We are looking for an embedded vision engineer to bring computer vision to [device]. You will take trained models and make them run within the memory, latency and power budget of [target hardware]. WHAT YOU WILL DO - Optimise and deploy vision models onto [target hardware] - Apply quantisation, pruning and compression while protecting accuracy - Profile and improve end-to-end pipeline performance on device - Integrate with camera interfaces and the image pipeline - Work with the modelling team on architectures that suit the hardware WHAT WE ARE LOOKING FOR - Strong C and C++ - Experience deploying neural networks on embedded or mobile hardware - Familiarity with at least one inference runtime (TensorRT, TFLite, ONNX Runtime, OpenVINO) - Practical profiling and optimisation experience on target devices NICE TO HAVE - Experience with [your specific SoC or platform] - Camera and ISP pipeline knowledge - Quantisation-aware training - DSP, NPU or FPGA acceleration experience ABOUT US [Two or three sentences on the company, the product, and why the problem matters.] DETAILS - Location: [city / hybrid / remote] - Salary: [range] - Apply: [link]
Calibrating the level
Adjust the requirements to match the level you are actually hiring for. Asking for senior capability at a mid-level budget is the most common cause of a stalled search.
Junior
Ports and benchmarks models on target hardware under guidance.
Mid
Owns the optimisation path from trained model to shipped device performance.
Senior
Chooses the hardware and architecture to meet the product constraint.
Staff / Principal
Sets edge strategy across a product line and influences silicon selection.
Post it where the right people are
JobsInVision is a computer vision job board — your listing reaches engineers who work in this field specifically, rather than a general software audience.