Giskard
Tech Team
On the technical side, our product is a full-stack application with a Python & Java backend and Vue.js frontend in Typescript. Machine learning models are at the heart of Giskard so the backend interacts with state-of-the-art ML libraries.
We are Open-Source, so we value a lot team members who have previous experience contributing to Open-Source software projects.
As a technical experts, you will:
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Build our product by designing and implementing its features both on the backend and frontend
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Work closely with Machine Learning libraries like scikit-learn, tensorflow, torch and transformers
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Be at the forefront of the Responsible AI new wave of innovation
Distribution of employees
Engineering
60%
Product
20%
Research
20%
350
automated tests
Technologies and tools
Spring-Boot
100%Python
100%Java
100%gRPC
100%Vue.js
100%TypeScript
100%OVH
100%GitHub
100%GCP GCE
100%Docker
100%AWS
100%
Backend
Frontend
Devops
GitHub
As our product is Open-Source, we build it in public https://github.com/Giskard-AI/giskard
Machine Learning
Our backend interacts with all the newest ML libraries : Huggingface, PyTorch, Tensorflow, etc.
Python
We offer an open-source Python library to scan & test AI models automatically
Organization and methodology
We rely on the Kanban methodology for product development. Tasks usually vary from full-stack development to machine-learning model integration. The development team is composed of R&D engineers and ML researchers. Our roadmap is heavily influenced by the demands of our customers. When each project emerges, we organize brainstorming that every developer can contribute to.
Product, project or technical challenge
Check the quality of AI models for Computer Vision
CV are one of the most widely used high-risk AI. For instance, it’s used for diagnosis using medical images to help doctors identify diseases at an early stage. Given the criticality, these models require special attention to bias & error mitigation by using highly-reliable quality testing. This projects aims to build the integration of CV models so that Giskard is able to inspect & test them. Three particular CV model types will be integrated : image classification, object detection and image segmentation.
This will require building ergonomic interfaces to inspect complex images and adapt Giskard test suites.
Check the biases of large NLP generation models
Over the last 2 years, the biggest AI breakthrough has been Large Language Models (LLMs) such as ChatGPT and Mistral. Despite their performance, these models raise many robustness and ethical challenges.
This project aims to integrate LLMs so that Giskard is able to inspect & test them. This requires computing metrics to measure distances between the predicted text and the right text output, custom filtering process to select examples and solutions to compute explanations of predictions.
This also requires building interfaces that enable ML engineers to design prompts, display text outputs and provide feedback on specific words.
Recruitment process
You can get an offer with salary & equity in 3 weeks 🚀
- Fit interview: 15 minutes
- Tech exercise: 10 days to complete
- Tech interview: 45 minutes
- Reference calls: 2 persons
- Final interview: 45 minutes
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