Robotics Junior Engineer

The Inria Grenoble - Rhône-Alpes research center groups together almost 600 people in 22 research teams and 7 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area.

 Inria Grenoble - Rhône-Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

RobotLearn aims to develop socially assistive robots with the capacity to perform multi-person interactions and open-domain dialogue. We conceive innovative methods and algorithms for computer vision, audio processing, and sensor-based control for socially-aware robots based on modern statistical- and deep learning to ground the required social robot skills.

Our scientific ambition of RobotLearn is to train robots to acquire the capacity to look, listen, learn, move, and speak in a socially acceptable manner. This is being achieved via a fine tuning between scientific findings, development of practical algorithms and associated software packages, and thorough experimental validation. RobotLearn team members plan to endow robotic platforms with the ability to perform physically unconstrained and open-domain multi-person interaction and communication.

The roadmap of RobotLearn is twofold: (i) to develope machine learning techniques for the temporal and spatial alignment of audio and visual data, variational Bayesian methods for unimodal and multimodal tracking of humans, and deep learning architectures for audio and audio-visual speech enhancement, and (ii) to explore novel scientific research opportunities at the crossroads of discriminative and generative deep learning architectures, Bayesian learning and inference, computer vision, audio/speech signal processing, spoken dialog systems, and robotics.

The paramount applicative domain of RobotLearn is the development of multimodal and multi-party interactive methodologies and technologies for social (companion) robots.

The RobotLearn team offers a development engineer position to work in the field of multi-modal human-robot interaction. The recruited engineer will have the following missions: port the deep learning and other algorithms and software onto a robotic platform; conduct experiments involving the robotic platform and participants; ensure software robustness and re-usability; prepare software for data collection and data annotation. The recruited engineer will work in close collaboration with the group members (researchers, PhD students, and development engineers).

• Developing software for robotic applications that is robust and re-usable.
• Porting the software onto a robotic platform and validate it via experiments.
• Update existing software to exploit recent features of 3rd party software such as PyTorch and ROS.

The candidates should have excellent programming skills in Python, and be fluent in English, both written and spoken. The following expertise is highly welcome: working with ROS (1 and 2) and developing ROS modules, software development using PyTorch, C++, working with Linux (command line, shell scripting), and cooperative software development (Git, CI, testing, …).

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage