bertdb

Uber | https://www.uber.com/ai-solutions | Senior Product Engineer | Full Time | Amsterdam, The Netherlands | Hybrid

We’re building a complex data labeling platform for AV and robotics teams working with multi-sensor data: images, point clouds, sequential data, and 3D scenes.

We’re hiring a Senior Product Engineer to help us ship the core web product faster and improve the quality of customer-facing labeling workflows.

Tech stack: Vue/React, TypeScript, Three.js/WebGL/WebGPU, TinyBase, Django.

You can help us solve problems like: - Making 2D and 3D annotation workflows faster, more precise, and easier to use - Designing point cloud tiling, autosave, undo/redo, review, filtering, and issue-tracking workflows that hold up under real labeling operations - Improving performance where milliseconds matter: rendering, local state updates, preloading, and geometric operations - Shaping API contracts, data models, permissions, background jobs, and operational tooling as the platform scales

This is a senior role with high ownership on a small team. To apply, please reach out to bert@segments.ai (we recently got acquired by Uber) with a resume and mention HN in your application.

Did you do any inpainting experiments? I can imagine a pixel-space diffusion model to be better at it than one with a latent auto-encoder.

Could the NeRF be replaced with a voxel grid, backpropagating to the voxel color values directly? Or is there a reason that wouldn't work?

Any open source projects with a nice Django/DRF (+ Vue/React frontend) codebase, that roughly follow this style guide?

Thanks! Would be great to learn more about your use case, we'll be in touch with you.

That book is great if you want to go in-depth! If you're a practitioner who wants to get to a trained model as quickly as possible, you're probably better of just following a tutorial. The official Keras tutorial on segmentation looks pretty good [1]. We also have a blog post with code samples on how to set up an image segmentation workflow with Segments.ai and Facebook's detectron2 framework [2].

[1] https://keras.io/examples/vision/oxford_pets_image_segmentat...

[2] https://segments.ai/blog/speed-up-image-segmentation-with-mo...

The segments you see on the screen are generated by our ML model. If your data is very noisy, our out-of-the-box model might not be the best fit. We can always improve performance by training a custom model for you on a small set of manually labeled data though.

Thanks for your feedback!

1. If the segment you start dragging from is already selected, all the segments you drag through will get deselected, and vice versa.

2. Did you try changing the granularity of the segments by scrolling your mouse wheel? We've had good experiences with microscopic imagery before, happy to connect and dig a bit deeper.

Happy to listen to what you need, feel free to shoot me an email.

Thanks Brian!

Looking forward to hearing your feedback when you give it a try!

Thanks! The existing tools on the market for image segmentation are not very sophisticated, so it's a niche where we can immediately make a difference.

In a sense, image segmentation labels are strictly more informative than bounding box labels: you can trivially extract the containing bounding box from a segmentation mask. One big reason that segmentation labels are not used more often, is simply because they are too expensive. Labeling a bounding box requires only two clicks, while labeling a segmentation mask requires much more time with manual tools. We're trying to solve that problem.

In the future we want to dig even deeper into this problem, and expand our scope to video and 3D segmentation labeling. We believe there will be a huge need for such tools now that everyone is getting smartphones with Lidar and AR/VR capabilities in their pockets.

Scale only provides labeling services and does not provide labeling software that you can use with your own in-house or dedicated workforce team. We offer both, and our labeling service is even a bit cheaper thanks to our focus on speeding up labeling with our technology.

Our biggest differentiator is our strong focus on image segmentation: we've put a lot of effort in creating a labeling interface that is optimized to speed up segmentation labeling, a task that is notoriously slow and expensive. Another thing we do differently is that we allow unlimited labeling for free in public datasets.

Acquarium is focused more on exploring and curating your data. It integrates with external labeling providers, like us.

The AI equivalent of this:

Growing Neural Cellular Automata - Differentiable Model of Morphogenesis

https://distill.pub/2020/growing-ca/