Robotics AI | Computer Vision | Edge Deployment Most perception roles are about improving model performance in controlled conditions.
This one is about building perception systems that work in the real world.
I’m working with a fast‑growing robotics AI company that is building advanced perception and autonomy technology for physical environments where reliability, latency and deployment quality genuinely matter.
They are looking for a Perception Engineer to take ownership of core computer vision systems across detection, tracking, re‑identification, scene understanding and edge deployment.
This is not a narrow research role, and it is not a role where you hand models over and hope they work later.
You will be building systems that need to run live, handle messy real‑world data, operate across multiple sensor streams, and continue performing outside the lab.
The role You will work on the perception stack behind a real‑world robotics AI platform, helping turn modern computer vision models into reliable deployed systems.
The work will sit across model development, optimisation, deployment and system‑level thinking. You will be close to the product, close to the engineering team, and close to the practical challenges that come with deploying AI into physical environments.
This would suit someone who enjoys building useful systems, not just training models.
What you will be working on
Building and improving perception pipelines for real‑world robotic and sensor‑based systems
Developing detection, tracking and re‑identification models for complex visual environments
Working with high‑throughput video and multi‑stream perception data
Applying modern computer vision and vision‑language techniques to scene understanding
Optimising models for latency, robustness and edge deployment
Taking models from prototype stage into production‑quality systems
Working closely with autonomy, robotics, software and infrastructure engineers
Debugging real‑world failure modes and improving system reliability over time
What they are looking for Strong practical experience in computer vision and perception engineering. Ideally experience across several of the following:
Computer vision, machine learning or deep learning applied to real‑world systems
Object detection, multi‑object tracking, re‑identification or video understanding
Experience working with live video, sensor data or deployed perception systems
Strong Python and practical ML engineering experience
Experience with PyTorch or similar deep learning frameworks
Model optimisation, deployment or inference on edge hardware
Comfortable working across research, engineering and product constraints
Able to take ownership of ambiguous technical problems
Strong communication skills and a low‑ego engineering approach
Additional experience (Helpful, not essential)
Robotics, autonomous systems or embodied AI
Multi‑camera or multi‑sensor perception
Vision‑language models or open‑vocabulary perception
C++, CUDA, TensorRT, ONNX or similar deployment tooling
Experience working in early‑stage or fast‑moving technical teams
Exposure to outdoor, industrial or unstructured environments
Why this is worth considering This is a chance to join an ambitious robotics AI company at an early stage, where the perception work will have a direct impact on the product and technical direction. You would be working on systems that need to be fast, reliable and deployable, not just impressive in a demo. The team is technical, ambitious and building for real‑world use cases, with a strong focus on solving difficult engineering problems rather than adding unnecessary process.
#J-18808-Ljbffr
This one is about building perception systems that work in the real world.
I’m working with a fast‑growing robotics AI company that is building advanced perception and autonomy technology for physical environments where reliability, latency and deployment quality genuinely matter.
They are looking for a Perception Engineer to take ownership of core computer vision systems across detection, tracking, re‑identification, scene understanding and edge deployment.
This is not a narrow research role, and it is not a role where you hand models over and hope they work later.
You will be building systems that need to run live, handle messy real‑world data, operate across multiple sensor streams, and continue performing outside the lab.
The role You will work on the perception stack behind a real‑world robotics AI platform, helping turn modern computer vision models into reliable deployed systems.
The work will sit across model development, optimisation, deployment and system‑level thinking. You will be close to the product, close to the engineering team, and close to the practical challenges that come with deploying AI into physical environments.
This would suit someone who enjoys building useful systems, not just training models.
What you will be working on
Building and improving perception pipelines for real‑world robotic and sensor‑based systems
Developing detection, tracking and re‑identification models for complex visual environments
Working with high‑throughput video and multi‑stream perception data
Applying modern computer vision and vision‑language techniques to scene understanding
Optimising models for latency, robustness and edge deployment
Taking models from prototype stage into production‑quality systems
Working closely with autonomy, robotics, software and infrastructure engineers
Debugging real‑world failure modes and improving system reliability over time
What they are looking for Strong practical experience in computer vision and perception engineering. Ideally experience across several of the following:
Computer vision, machine learning or deep learning applied to real‑world systems
Object detection, multi‑object tracking, re‑identification or video understanding
Experience working with live video, sensor data or deployed perception systems
Strong Python and practical ML engineering experience
Experience with PyTorch or similar deep learning frameworks
Model optimisation, deployment or inference on edge hardware
Comfortable working across research, engineering and product constraints
Able to take ownership of ambiguous technical problems
Strong communication skills and a low‑ego engineering approach
Additional experience (Helpful, not essential)
Robotics, autonomous systems or embodied AI
Multi‑camera or multi‑sensor perception
Vision‑language models or open‑vocabulary perception
C++, CUDA, TensorRT, ONNX or similar deployment tooling
Experience working in early‑stage or fast‑moving technical teams
Exposure to outdoor, industrial or unstructured environments
Why this is worth considering This is a chance to join an ambitious robotics AI company at an early stage, where the perception work will have a direct impact on the product and technical direction. You would be working on systems that need to be fast, reliable and deployable, not just impressive in a demo. The team is technical, ambitious and building for real‑world use cases, with a strong focus on solving difficult engineering problems rather than adding unnecessary process.
#J-18808-Ljbffr
