Engineering Manager – R&D Automation

Engineering Manager – R&D Automation 

A fantastic career opportunity for a skilled Engineering Manager to join an leading Quantum AI company, where you will be working alongside world-leading experts to develop solutions that tackle real-world challenges. We’re looking for passionate individuals eager to grow in an ethics-driven environment that values sustainability and diversity.

This is initially a Fixed Term Contract, with Hybrid working from sites in Madrid or Barcelona. 

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.
• 5–8 years of experience in software engineering, including 2+ years in a leadership or tech lead role.
• Strong project management skills, including sprint planning, roadmap creation, and cross-functional coordination.
• Proven experience managing cross-functional teams (backend, frontend, or full stack) delivering complex systems.
• Excellent written and verbal communication skills with both technical and non-technical stakeholders.
• Solid understanding of API-driven architectures, micro-services, and cloud-native environments (AWS or GCP).
• Demonstrated ability to translate research needs into product requirements and scalable technical solutions.
• Hands-on experience with Python, Go, or TypeScript, and familiarity with FastAPI, React/Next.js, or similar frameworks.
• Deep appreciation for product quality, developer experience, and system reliability.
• Strong background in CI/CD, containerization (Docker/Kubernetes), and modern DevOps practices.

Preferred Qualifications

• Prior experience building or managing ML infrastructure, workflow orchestration, or AI developer tools (e.g., Flyte, MLflow, Hugging Face Hub).
• Understanding of LLM lifecycle — from fine-tuning and evaluation to deployment and observability.
• Exposure to R&D automation, internal tooling, or data platform engineering.
• Experience mentoring engineers through system design, performance optimization, and scaling challenges.
• Product-oriented mindset: comfort balancing R&D iteration speed with engineering rigor and user experience.
• Interest in creating an environment where ML engineers and researchers can focus on innovation, not infrastructure.