NAVER LABS Europe is part of the R&D division of NAVER, Korea’s leading Internet portal and a global tech company with a range of services that include search, commerce, content, fintech, robotics and cloud.
The Optimization with Learning team at NAVER LABS Europe conducts research at the intersection of machine learning and mathematical optimization, with a focus on sequential decision making, combinatorial optimization, and multi-agent coordination. Our work is motivated by challenging real-world robotics problems, in particular large-scale robot fleet coordination tasks in uncertain and dynamic environments. Our goal is to develop principled learning-based approaches for sequential decision making and combinatorial optimization that generalize beyond a single application and advance the broader research field. Through close collaborations with robotics teams across NAVER LABS, researchers have the opportunity to connect fundamental research questions with real operational problems. This creates a unique environment to pursue ambitious research directions, publish in leading conferences, and contribute to emerging AI-driven robotics systems.
We're looking for early-career researchers and recent PhD graduates to join the team and contribute to the following research activities:
- Conduct research on machine learning approaches for sequential decision making and combinatorial optimization.
- Develop new models and algorithms, for example using deep reinforcement learning, graph neural networks, or other learning-based optimization techniques.
- Design and implement prototypes and proof-of-concept systems to evaluate new ideas and algorithmic approaches.
- Run and analyze large-scale experiments in simulation and realistic problem settings inspired by real robotic systems.
- Collaborate with researchers and engineers across NAVER LABS to transfer and demonstrate developed approaches on real robotic systems.
- Contribute to publications in leading conferences and journals in machine learning, artificial intelligence, optimization, and robotics.
Required qualifications
- PhD in machine learning, optimization, operations research, robotics, computer science, or a related field.
- Strong background in machine learning and/or sequential decision making.
- Excellent programming skills in Python, and experience with deep learning frameworks such as PyTorch.
- Experience in designing, implementing, and evaluating machine learning models.
- Strong interest in research problems related to decision making, combinatorial optimization, or multi-agent systems.
- Ability to work collaboratively in multidisciplinary research environments.
Preferred qualifications
- Experience with deep reinforcement learning, in particular multi-agent reinforcement learning.
- Experience with machine learning for structured data, such as graph neural networks or related approaches.
- Experience with combinatorial optimization, neural combinatorial optimization, or learning-augmented optimization methods.
- Publications in leading conferences in machine learning, artificial intelligence, optimization, or robotics (e.g., NeurIPS, ICLR, ICML, AAAI, IROS, ICRA).
- Experience with large-scale experiments, simulation environments, or real-world-inspired problem settings.
- Interest in connecting machine learning research with real-world applications, in particular in robotics systems.
The team regularly publishes in leading AI and robotics conferences. Recent publications include:
- Learning to Solve the Multi-Agent Task Assignment Problem for Automated Data Centers - IROS 2025
- GOAL: a Generalist Combinatorial Optimization Agent Learner - ICLR 2025
- Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses - ECAI 2024
- BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization - NeurIPS 2023
- We foster a collaborative environment dedicated to ambitious, multidisciplinary projects that translate advanced research into impactful, real-world solutions, supported by 30+ years of experience in AI and related fields.
- Flexible work/life balance.
We are an equal opportunity employer that hires based on skills, experience, and merit. We foster an inclusive and diverse workplace where all qualified candidates are considered fairly, regardless of background.
We’re based in Meylan, close to Grenoble, a city that offers the perfect balance of urban life, cutting-edge research and technology, and spectacular mountain landscapes that provide countless opportunities to relax, recharge, and enjoy the outdoors.
All applications will be carefully considered, even if not all required skills are met. We value diverse backgrounds and the potential of each candidate, and we offer training to support the development of necessary skills.
NAVER LABS, co-located in Korea and France, is the organization dedicated to preparing NAVER’s future. Scientists at NAVER LABS Europe are empowered to pursue long-term research problems that, if successful, can have significant impact and transform NAVER. We take our ideas as far as research can to create the best technology of its kind. Active participation in the academic community and collaborations with world-class public research groups are, among others, important ways to achieve these goals. Teamwork, focus and persistence are important values for us.
When applying for this position online, please don't forget to upload your CV and cover letter. Incomplete applications will not be considered.
NAVER LABS Europe is subject to French jurisdiction requiring organisations to stipulate that a job/internship is open to both women and men. None of our jobs/internships are gender specific.
