Software Engineer - AI Research Prototyping F/M

📍 Job Overview

Job Title: Software Engineer - AI Research Prototyping F/M

Company: NAVER LABS Europe

Location: Meylan, Auvergne-Rhône-Alpes, France

Job Type: Full-time

Category: AI/ML Engineering, Software Development, Research & Development

Date Posted: April 15, 2026

Experience Level: Mid-Level (2-5 years)

Remote Status: On-site

🚀 Role Summary

• Develop and implement prototypes, demonstrators, and reusable software components based on cutting-edge AI research in Robotics.

• Collaborate closely with research teams to translate innovative AI and ML concepts into tangible, impactful systems and practical implementations.

• Leverage modern software development practices, including AI-assisted coding agents, to accelerate the development lifecycle.

• Manage a portfolio of short-to-medium-term projects, demonstrating autonomy and adaptability in transforming research ideas into working software.

• Integrate advanced machine learning models into functional applications and showcase research results through compelling technical demonstrations.

📝 Enhancement Note: This role is designed for an engineer who thrives in an exploratory, research-adjacent environment, bridging the gap between theoretical AI advancements and practical, demonstrable robotic capabilities. The emphasis on rapid prototyping and AI-assisted development suggests a dynamic and forward-thinking approach to software engineering within the AI and robotics domain.

📈 Primary Responsibilities

• Design, build, and maintain prototypes, demonstrators, and reusable software components that showcase AI research outcomes in robotics, computer vision, NLP, and optimization.

• Collaborate with AI and robotics researchers to understand complex research concepts and translate them into functional software systems and engaging technical demonstrations.

• Implement and integrate machine learning models (e.g., using PyTorch) into end-to-end prototypes, ensuring performance and scalability in GPU computing environments.

• Utilize modern software engineering practices, including robust testing, version control (e.g., Git), and software architecture principles, to ensure high-quality code.

• Explore and adopt new development tools and methodologies, including AI coding agents and other AI-assisted development techniques, to enhance productivity and development speed.

• Develop user interfaces, web applications, or REST APIs as needed to support demonstration and integration of research prototypes.

• Manage project timelines, deliverables, and communication for multiple concurrent prototyping projects, ensuring alignment with research goals.

• Document technical designs, implementation details, and demonstration procedures to facilitate knowledge sharing and future development.

📝 Enhancement Note: The core responsibility revolves around the practical application and demonstration of AI research. This involves not just coding but also understanding the research domain, creatively presenting findings, and iterating quickly based on feedback from researchers and potential stakeholders. The role is a crucial link in the R&D pipeline.

🎓 Skills & Qualifications

Education:

Experience:

Required Skills:

• Programming Proficiency: Expert-level Python programming skills are essential for rapid development and ML integration.

• Software Engineering Fundamentals: Solid understanding of core software engineering principles, including software architecture, design patterns, unit testing, integration testing, and validation practices.

• Version Control: Proficiency with version control systems, such as Git, for collaborative development and code management.

• Rapid Prototyping: Demonstrated ability to quickly explore new ideas, build proof-of-concepts, and develop functional prototypes with minimal guidance.

• Autonomy & Collaboration: Ability to work independently on challenging technical problems while effectively collaborating with cross-functional research teams.

• AI Development Interest: Keen interest in modern software development trends, particularly AI-assisted development, coding agents, and their application in accelerating software creation.

• Language Proficiency: Fluent written and spoken English, as it is the primary working language.

Preferred Skills:

• ML/AI Frameworks: Hands-on experience with machine learning frameworks like PyTorch, TensorFlow, or similar, and familiarity with ML workflows.

• GPU Computing: Experience working with GPU computing environments for model training and inference.

• Prototyping Technologies: Familiarity with technologies commonly used in building prototypes and demonstrations, such as web development (e.g., Flask, Django), mobile application development, user interface (UI) design, RESTful APIs, and containerization (Docker).

• Demo Design: Ability to design and implement clear, engaging, and effective technical demonstrations that highlight AI and robotics capabilities.

• Creative Problem-Solving: A creative mindset with an aptitude for identifying potential use cases and designing compelling demonstrations for research outcomes.

📝 Enhancement Note: The required skills emphasize a strong foundation in software engineering and Python, coupled with a proactive, experimental approach to development. Preferred skills highlight a desire for candidates who can hit the ground running with ML frameworks and have experience in technologies that facilitate rapid demonstration and deployment of research outputs.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrated Prototypes: Showcase at least 2-3 distinct software prototypes or demonstrators you have built, preferably related to AI, robotics, or complex system integration.

• Code Repository Access: Provide access to a GitHub, GitLab, or similar repository containing well-documented code samples that highlight your programming skills, architectural choices, and problem-solving approach.

• Process Improvement Case Studies: Include examples (even if informal) of how you've applied software engineering best practices or new technologies to improve development speed, system efficiency, or the quality of a project.

• Impact & Results: For each portfolio item, clearly articulate the problem addressed, your technical contributions, the technologies used, and the outcomes or impact achieved (e.g., performance improvements, successful demonstration, reusability).

Process Documentation:

• Workflow Design: Examples of how you've designed or optimized software development workflows for prototyping or research projects, focusing on speed and iteration.

• System Integration: Documentation or examples of integrating disparate software components or ML models into a cohesive system.

• Performance Analysis: Evidence of how you measure and analyze the performance of your prototypes or developed systems, especially in relation to research objectives.

📝 Enhancement Note: For this role, the portfolio is critical for demonstrating practical application of research. Candidates should focus on showcasing their ability to build functional systems quickly, integrate complex technologies, and present their work effectively. The emphasis is less on extensive, long-term product development and more on agile, impactful prototyping.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Health Insurance: Comprehensive health, dental, and vision insurance.

• Retirement Plan: Contribution to a French retirement savings plan.

• Paid Time Off: Generous annual leave, public holidays, and potential for additional paid time off.

• Professional Development: Budget for training, conferences, and workshops to enhance skills in AI, ML, and software engineering.

• Relocation Assistance: Support for candidates relocating to Meylan, France.

• Work-Life Balance: Emphasis on maintaining a healthy work-life balance.

• On-site Amenities: Access to on-site facilities, potentially including a canteen and recreational areas.

Working Hours:

• Standard full-time employment, typically around 39-40 hours per week, with flexibility offered where possible to accommodate project needs and individual work styles.

📝 Enhancement Note: The salary range is an estimate based on industry benchmarks for a mid-level software engineer in a specialized AI/Robotics R&D role in France. Actual compensation will be determined by individual experience, qualifications, and negotiation. Benefits are typical for a European tech R&D organization.

🎯 Team & Company Context

🏢 Company Culture

Industry: Artificial Intelligence, Robotics, Software Technology. NAVER LABS Europe operates at the forefront of AI research, aiming to develop next-generation technologies for intelligent systems and robots.

Company Size: NAVER LABS Europe is part of the larger NAVER Corporation, a significant global technology company. Within Europe, it is a dedicated research facility, likely comprising several hundred researchers and engineers, fostering a focused, innovative, and collaborative research environment.

Founded: NAVER LABS Europe was established to drive cutting-edge research. Its foundation is rooted in NAVER's commitment to innovation and technological advancement.

Team Structure:

• PMO Team: This role sits within the Project Management Office (PMO) team, which acts as a crucial bridge between pure research and applied technology.

• Cross-Functional Collaboration: The PMO team works intimately with multiple research domains (e.g., Computer Vision, NLP, Robotics, Optimization), requiring close collaboration with PhD researchers and senior scientists.

• Reporting: The Software Engineer will likely report to a PMO Lead or a Senior Project Manager, who oversees the prototyping and demonstration efforts.

Methodology:

• Agile Prototyping: Emphasis on rapid iteration, quick development cycles, and agile methodologies to bring research concepts to life swiftly.

• Data-Driven Experimentation: Leveraging research data and ML models to build and test functional systems, with a focus on measurable outcomes.

• Open Innovation: Encouraging the exploration of new technologies and approaches, including AI-assisted development, to push the boundaries of what's possible.

Company Website: https://www.naverlabs.com/

📝 Enhancement Note: NAVER LABS Europe is positioned as a hub for advanced AI research. The culture likely values innovation, scientific rigor, and practical application, with a strong emphasis on collaboration between engineers and researchers. The PMO team's role is vital in ensuring research impact.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Mid-Level Software Engineer specializing in AI Research Prototyping. It's a hands-on technical role focused on building and demonstrating advanced technologies rather than managing large teams or strategic operations.

Reporting Structure: The engineer will report to a Project Management Office (PMO) lead or manager, working closely with various research teams. This structure allows for direct contribution to research translation while being part of a supportive project management framework.

Operations Impact: The impact is direct and tangible: translating complex AI research into working prototypes and compelling demonstrations. This work is critical for showcasing research achievements, attracting talent, securing future funding, and potentially paving the way for product development by NAVER. The role directly contributes to the "impact" of research projects.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific AI domains (e.g., reinforcement learning for robotics, advanced computer vision techniques) through project work and dedicated learning.

• Prototyping & Demo Leadership: Evolve into a lead role for prototyping efforts on specific research projects, guiding junior engineers and taking ownership of complex demonstrators.

• Cross-Functional Expertise: Gain broad exposure to diverse AI research areas, enhancing versatility and marketability.

• Transition to Research Engineering: Potentially transition into more specialized research engineering roles or contribute to the engineering aspects of productization if research proves commercially viable.

• Continuous Learning: Access to internal and external training, conferences, and research publications to stay at the forefront of AI and robotics development.

📝 Enhancement Note: This role offers significant growth for engineers passionate about applied AI and seeing research come to life. The path is primarily technical, focusing on deepening expertise in AI/ML systems and advanced prototyping, with potential to lead specific project initiatives.

🌐 Work Environment

Office Type: NAVER LABS Europe is a research and development facility. The environment is expected to be modern, collaborative, and equipped with state-of-the-art technology.

Office Location(s): Meylan, France, situated in the Auvergne-Rhône-Alpes region, known for its technological and research hubs. The specific office will provide dedicated workspace, meeting rooms, and potentially specialized labs for robotics and AI development.

Workspace Context:

• Collaborative Spaces: Ample opportunities for informal and formal collaboration with researchers, fellow engineers, and project managers.

• Technology-Rich Environment: Access to powerful computing resources, including GPUs, development tools, and potentially robotics hardware for testing.

• Research-Focused Atmosphere: An environment that encourages intellectual curiosity, experimentation, and the pursuit of innovative solutions in AI and robotics.

Work Schedule: Full-time, typically 39-40 hours per week. While core hours will apply, there may be flexibility to accommodate project deadlines and personal work rhythms, fostering a productive and balanced work environment.

📝 Enhancement Note: The work environment is geared towards innovation and research. Candidates can expect a dynamic setting where collaboration, access to cutting-edge technology, and a focus on pushing AI boundaries are paramount.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or

Recruiter call to assess basic qualifications, motivation, and cultural fit.

• Technical Interview(s): One or more interviews with engineering or research leads. These will likely include:

  • Coding Challenges: Live coding exercises focusing on Python, data structures, and algorithms.
  • System Design: Discussions on designing prototypes, integrating ML models, and architectural considerations for research demonstrators.
  • Problem-Solving Scenarios: Hypothetical scenarios related to AI/robotics prototyping challenges.

• Portfolio Presentation: A dedicated session to present your portfolio, discuss your contributions, and explain technical decisions.

• Meet the Team/Manager: Final interviews with potential team members or the hiring manager to assess team fit, communication skills, and overall suitability.

Portfolio Review Tips:

• Curate Strategically: Select 2-3 of your strongest, most relevant projects that showcase your AI/ML, Python, and rapid prototyping skills.

• Focus on Impact: For each project, clearly articulate the problem, your role, the technical challenges, your solutions, and the results/impact. Use metrics where possible.

• Code Quality: Ensure any linked code repositories are clean, well-commented, and demonstrate good software engineering practices. Highlight specific sections if necessary.

• Demo Readiness: Be prepared to walk through a live demonstration or provide a concise video demonstration of your prototypes.

• Storytelling: Frame your portfolio items as compelling stories of innovation and problem-solving.

Challenge Preparation:

• Python Fundamentals: Brush up on Python syntax, common libraries (NumPy, Pandas), data structures, and object-oriented programming.

• AI/ML Concepts: Review fundamental ML concepts, common algorithms, and the lifecycle of an ML project. If your background is strong in PyTorch, be ready to discuss its nuances.

• Software Architecture: Prepare to discuss principles of software design, trade-offs, and how to build scalable and maintainable systems, especially for research prototypes.

• Problem-Solving: Practice breaking down complex problems into smaller, manageable parts and articulating your thought process clearly.

📝 Enhancement Note: The interview process is designed to thoroughly assess both technical prowess and the ability to translate research into practical, demonstrable systems. A strong, well-presented portfolio is crucial for this role.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary), potentially C++ for performance-critical components.

• Version Control: Git (e.g., GitHub, GitLab).

• Development Environments: IDEs like VS Code, PyCharm.

• Containerization: Docker for consistent development and deployment environments.

Analytics & Reporting:

• ML Frameworks: PyTorch (preferred), TensorFlow, scikit-learn.

• Data Science Libraries: NumPy, Pandas, Matplotlib, Seaborn.

• Experiment Tracking: Tools like MLflow or Weights & Biases (potentially).

CRM & Automation:

• Not directly applicable in a pure R&D prototyping role, but familiarity with CI/CD pipelines (e.g., Jenkins, GitLab CI) for automated testing and deployment of prototypes is a plus.

• APIs: Experience with RESTful API development and consumption.

📝 Enhancement Note: The technology stack is heavily focused on Python, ML frameworks, and tools that support rapid development and experimentation. Familiarity with containerization and API development is key for creating functional demonstrators.

👥 Team Culture & Values

Operations Values:

• Innovation: A strong drive to explore new ideas and push the boundaries of AI and robotics research.

• Excellence: Commitment to high-quality engineering and rigorous scientific exploration.

• Collaboration: A belief in the power of teamwork and cross-disciplinary synergy between researchers and engineers.

• Impact: A focus on translating research into tangible outcomes that demonstrate value and potential.

• Curiosity: An intrinsic desire to learn, experiment, and understand complex systems.

Collaboration Style:

• Research-Engineering Synergy: Close, iterative collaboration between software engineers and AI/robotics researchers, fostering mutual understanding and shared goals.

• Open Communication: Encouraging open dialogue, constructive feedback, and knowledge sharing across teams.

• Agile Teamwork: Working in agile sprints or cycles for prototyping, with regular check-ins and collaborative problem-solving sessions.

📝 Enhancement Note: The culture values a blend of scientific curiosity and engineering pragmatism. Team members are expected to be proactive, collaborative, and driven by the desire to make a significant impact on the field of AI and robotics through practical application.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Research Gaps: Translating highly theoretical or early-stage research into functional, demonstrable prototypes can be challenging due to inherent uncertainties and complexity.

• Rapid Iteration Demands: The need for rapid development and frequent iteration requires adaptability and efficiency under tight timelines.

• Diverse Problem Domains: Working across multiple research domains (CV, NLP, Robotics) demands quickly acquiring knowledge and understanding in new areas.

• Keeping Pace with AI Advancements: The field of AI evolves at an unprecedented pace, requiring continuous learning and adaptation to new techniques and tools.

Learning & Development Opportunities:

• AI/ML Specialization: Opportunities to deepen expertise in specific AI subfields relevant to robotics through hands-on projects and access to research knowledge.

• Advanced Prototyping Techniques: Learning and applying state-of-the-art methods for rapid system development and demonstration.

• Exposure to Leading Research: Direct involvement with world-class AI researchers, gaining insights into the future of the field.

• Industry Conferences & Training: Support for attending relevant conferences (e.g., ICRA, CVPR, NeurIPS) and pursuing specialized training.

📝 Enhancement Note: The primary challenges stem from the inherent complexity of AI research and the need for rapid translation into working systems. Growth opportunities are tied to technical mastery within AI/robotics and the ability to navigate cutting-edge research.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex AI/ML research concept you've had to translate into a functional system. What were the biggest challenges, and how did you overcome them?"

  • Preparation: Prepare a specific example from your portfolio or experience. Focus on the translation process, your problem-solving approach, and the technical solutions you implemented. Emphasize your ability to simplify complex ideas for practical application.

• "How do you approach building a prototype quickly while ensuring it's robust enough to demonstrate key research findings effectively?"

  • Preparation: Discuss your rapid prototyping methodologies, prioritization techniques, and how you balance speed with quality. Mention tools and practices like iterative development, modular design, and focused testing.

• "Imagine you need to build a demo showcasing a new computer vision algorithm for object detection on a robot. What steps would you take, and what technologies would you consider?"

  • Preparation: Outline your thought process, from understanding the algorithm's requirements to selecting appropriate hardware (if applicable), software stack (Python, ML frameworks, visualization tools), and demo presentation strategy.

Company & Culture Questions:

• "What interests you most about NAVER LABS Europe's research in AI for Robotics, and how do you see your skills contributing?"

  • Preparation: Research NAVER LABS Europe's specific projects and publications. Connect your skills and interests directly to their stated research areas and goals.

• "Describe your experience working with researchers or scientists. How do you ensure effective communication and collaboration across different technical backgrounds?"

  • Preparation: Provide examples of successful collaborations. Highlight your communication strategies, active listening skills, and ability to bridge technical language barriers.

• "How do you stay updated with the rapidly evolving field of AI and machine learning?"

  • Preparation: Mention specific resources like research papers (arXiv), conferences, online courses, technical blogs, and communities you follow.

Portfolio Presentation Strategy:

• Structure is Key: For each project, use a clear narrative: Problem -> Your Solution -> Technical Details -> Results/Impact.

• Highlight Your Role: Be specific about your contributions, especially in collaborative projects.

• Show, Don't Just Tell: Prepare live demos or concise video walkthroughs. If live, ensure you have a stable environment.

• Quantify Impact: Use metrics (e.g., "reduced processing time by X%", "achieved Y% accuracy") whenever possible.

• Technical Depth: Be ready to dive deep into the technical aspects of your projects when asked. Explain your choices of algorithms, architectures, and tools.

📝 Enhancement Note: Preparation should focus on demonstrating practical application of AI/ML, strong Python and software engineering skills, effective collaboration, and a genuine passion for the research domain. The portfolio is your primary tool for showcasing these capabilities.

📌 Application Steps

To apply for this Software Engineer position:

• Submit your application through the provided link on careers.werecruit.io.

• Tailor Your Resume: Highlight Python proficiency, software engineering practices, experience with AI/ML frameworks (especially PyTorch), and any prototyping or demonstration projects. Use keywords from the job description.

• Prepare Your Portfolio: Select 2-3 of your strongest projects. Ensure code repositories are accessible and well-documented, and prepare to walk through your contributions and technical decisions.

• Research NAVER LABS Europe: Understand their research focus areas, recent publications, and the company's mission. Prepare to articulate why you are a good fit for their specific R&D environment.

• Practice Interview Questions: Rehearse answers to technical, behavioral, and situational questions, focusing on demonstrating your problem-solving skills and collaborative approach.

⚠️ Important Notice: This enhanced job description provides a comprehensive overview and AI-generated insights based on industry standards. All details, including specific responsibilities and requirements, should be confirmed with NAVER LABS Europe during the application and interview process.