Real-Time One-Shot Vision Engineer for Robotics

Lili-o; Paris; On-site; EUR 70,000 - 110,000; Full time

Job summary

Lili-O in Paris is seeking a Computer Vision Engineer to push forward our one-shot learning approach, collaborating with our science team to build vision models and dense descriptors that operate in real time on robot hardware. You will work on affordance learning, dense descriptors, and real-time flow matching, partnering with researchers to turn results into deployable vision modules on real hardware.

You will research and develop object segmentation across unstructured environments and

Qualifications

• PhD or strong research background in computer vision / machine learning appreciated.
• Experience taking vision research from prototype to real-time on-robot deployment is a plus.
• Comfortable working in a purely vision-based paradigm with no reliance on markers or hand-crafted priors.

Responsibilities

• Research and develop vision models for one-shot learning of new objects and tasks.
• Work on affordance learning - teaching the robot where and how it can interact with an object from a single demonstration.
• Develop and refine dense descriptors for robust object/keypoint representation across viewpoints.
• Build object segmentation pipelines that hold up in unstructured, real-world environments.
• Explore flow matching approaches for vision-driven generative/representation models.
• Optimize models for real-time inference on robot hardware.
• Collaborate closely with the research team on research direction, experiments, and turning results into shippable vision modules.

Skills

One-shot learning

Affordance learning

Dense descriptors

Real-time inference

Flow matching

Python

Deep learning

Neural network architectures

Education

PhD or strong research background in CV/ML

Tools

PyTorch

Job description

Lili-O in Paris is seeking a Computer Vision Engineer to push forward our one-shot learning approach, collaborating with our science team to build vision models and dense descriptors that operate in real time on robot hardware. You will work on affordance learning, dense descriptors, and real-time flow matching, partnering with researchers to turn results into deployable vision modules on real hardware.

You will research and develop object segmentation across unstructured environments and