# Computer Vision Engineer, Neural ISP & Depth

Canonical page: https://efference.ai/careers/research-engineer-neural-isp/

Computer Vision Engineer, Neural ISP & Depth at Efference. San Francisco · Full time · On-site. You'll develop neural image-processing and depth models that run on Efference devices and improve robot perception.

You'll develop neural image-processing and depth models that run on Efference devices and improve robot perception. You'll own the work from training data and model design through NPU deployment, checking that gains in image quality or depth accuracy hold up in real robotic tasks.

## What you’ll do

- Develop neural and hybrid ISP pipelines for RAW processing, denoising, demosaicing, HDR, and other imaging problems where learning improves the system.
- Build monocular, stereo, or multi-camera depth models with explicit evaluation of scale, boundary quality, temporal consistency, and uncertainty.
- Construct training and validation datasets, loss functions, and ablations that expose behavior under motion, low light, reflective surfaces, and unfamiliar scenes.
- Optimize architectures, quantization, operator support, memory use, and data movement for our NPU; profile performance on the actual device.
- Measure geometric fidelity and failure modes, including invented detail or unstable depth, with image-quality, VIO, and robotics evaluation engineers.
- Integrate models into versioned camera pipelines and maintain repeatable comparisons of task performance, runtime, power, and bandwidth.

## What we are looking for

- Experience developing deep-learning models for image restoration, computational imaging, stereo, depth estimation, or related geometric vision tasks.
- Strong Python and PyTorch skills, with experience building training pipelines and running controlled experiments.
- Understanding of image formation, camera geometry, and the classical processing methods relevant to your models.
- Experience optimizing or deploying models under real compute, memory, or latency constraints.
- Ability to analyze failure cases and distinguish a better benchmark score from an improvement that matters in a deployed perception system.

## Nice to Have

- Direct experience with RAW/Bayer data, ISP tuning, multi-view calibration, or temporal video models.
- Work on NPU toolchains, quantization-aware training, custom operators, or hardware-aware architecture design.
- Research or production work in robot perception, learned compression, or joint optimization of imaging and downstream tasks.