Senior AI Research Engineer

About the Role

This position is offered through a partner company based in Germany, seeking a Senior AI Research Engineer to enhance and expand infrastructures for cutting-edge AI applications in industrial automation. This opportunity allows you to contribute at the crossroads of machine learning engineering, MLOps, and pioneering research by facilitating accelerated production deployment of advanced AI models and workflows.

Key Responsibilities

• Lead the full lifecycle management of research infrastructure including experiment orchestration, distributed training, model evaluation, and automated deployment systems.
• Design and scale distributed computing platforms supporting complex machine learning tasks, covering multi-node GPU clusters, data pipelines, and streamlined cloud resource management.
• Enhance development throughput by optimizing simulation tools, refining training processes, and addressing performance bottlenecks to implement scalable engineering solutions.
• Serve as a liaison between research scientists and production engineers, translating AI research into robust and reliable production-ready systems.
• Develop comprehensive knowledge of internal tools and platforms to better support client-focused technical solutions.
• Create and maintain thorough documentation related to research projects, engineering decisions, products, and operational workflows.
• Participate in strategic technical planning impacting the medium- and long-term evolution of research infrastructure and engineering approaches.
• Take full ownership of projects from ideation through to successful delivery, managing priorities and execution.
• Mentor peers and promote knowledge sharing and collaborative problem-solving within engineering teams.
• Continuously enhance development methodologies, tooling, and infrastructure to expedite AI research and deployment efforts.

Required Qualifications

• A minimum of four years of relevant professional experience in software engineering, machine learning engineering, MLOps, or related domains.
• Proven track record in leading technical projects from conception through deployment.
• Experience within machine learning R&D settings, preferably with a focus on integrating research outputs into production environments.
• Robust understanding of core concepts in machine learning and MLOps, including model lifecycle management, experiment tracking, deployment pipelines, and handling non-deterministic system components.
• Strong programming expertise primarily in Python, with knowledge of C++ or Rust being advantageous.
• Solid foundation combining engineering principles and scientific insight in machine learning, optimization, control theory, or physical sciences.
• Expertise in designing scalable distributed computing infrastructures tailored for AI workloads and cloud environments.
• Excellent analytical skills, intellectual curiosity, and openness to exploring diverse technical areas.
• Effective communication, organization, and collaboration capabilities, especially in remote and multicultural teams.
• Commitment to core values such as transparency, accountability, collaboration, operational excellence, and empathy.

Preferred Skills and Experiences

• Hands-on experience with machine learning research, AI implementations, or MLOps-centric engineering roles.
• Background in reinforcement learning, simulation software, or control systems engineering.
• Familiarity with distributed computing frameworks like Ray and managing multi-node GPU processing.
• Proficiency using technologies such as PyTorch, SciPy, scikit-learn, NumPy, pandas, MLflow, Docker, Kubernetes, and cloud solutions.
• Understanding of industrial domains including HVAC systems, manufacturing processes, or data center operations.
• Academic or technical preparation that aids close cooperation with research teams.

Employee Benefits

• Competitive annual salary ranging between £92,065 and £173,648, influenced by geographic location, experience, and credentials.
• Potential eligibility to participate in equity programs.
• Fully remote work setup supporting global collaboration across different time zones.
• Comprehensive coverage for medical, dental, and vision care, subject to regional variations.
• Unlimited paid leave policies with a mandatory minimum of twenty days annually.
• Paid parental leave conforming to regional statutes.
• Flexible allowances for workspace arrangements, wellness activities, and ongoing professional education.
• Provision of a company MacBook for work-related needs.
• Access to training and development programs encompassing technical skills, customer engagement, and career advancement.
• A dynamic, fast-moving work environment that values impactful contributions and technical influence.
• A strong culture of remote work prioritizing documentation, asynchronous workflows, routine communication, and virtual team-building.
• Significant autonomy and opportunity to contribute to meaningful AI-driven technological advances.

Additional Information

Applications are managed by the partner company. The recruitment process involves AI-assisted candidate evaluation based on qualifications and suitability, but decision-making remains human-driven. Applicants should note that their personal data will be processed under legitimate interest and applicable data protection laws, including GDPR where relevant. Candidates may inquire about their data handling and rights related to their application.