Applied Scientist / AI Research Engineer

About Zinkworks

Zinkworks collaborates with top Telecommunications and Financial Services firms to transform legacy infrastructures, migrate vital platforms to cloud environments, and develop AI-powered automation solutions. Their expertise spans OSS transformation, rApp development, and network intelligence. With offices in Ireland and operations extending across the EU, UK, and US, Zinkworks combines specialized domain knowledge with delivery excellence to accelerate modernization and enhance operational efficiency.

Role Overview

Zinkworks is assembling a small team focused on creating advanced AI and machine learning solutions for next-generation telecom networks. The team takes innovative product ideas through all development stages, from initial concepts to functional prototypes.

The company seeks a senior applied scientist to lead the development of machine learning methodologies underpinning these products. This role involves addressing complex network data challenges by selecting optimal modeling techniques, evaluating trade-offs, and creating validated prototype models. A hands-on position, it requires both research acumen and strong engineering skills to implement solutions effectively.

Key Responsibilities

• Lead the selection and justification of machine learning models for the most challenging problems faced by the team.
• Translate ambiguous, real-world issues into robust machine learning approaches and develop prototypes to validate these methods.
• Critically evaluate and challenge modeling decisions within the team, maintaining rigorous standards where assumptions are backed by solid evidence.
• Customize state-of-the-art academic techniques to fit the complexities of real-world operational data.
• Collaborate closely with domain experts to accurately frame problems and with engineering teams to prepare prototypes for production deployment.

Candidate Requirements

• Proven expertise in ML modeling with hands-on experience designing, training, and critically assessing models, particularly within the domains of graph learning, spatio-temporal/time-series modeling, reinforcement learning, and causal inference.
• Experience taking complex or ill-defined problems from concept through to validated, working ML models.
• Strong proficiency in Python and engineering practices; capable of prototyping on cloud-based ML platforms, preferably Google Cloud Platform or Vertex AI.
• Excellent communication skills to clearly justify technical decisions to both technical and non-technical stakeholders and willingness to revise approaches based on evidence.
• Ability to operate effectively in a fast-paced exploratory environment with minimal support, driving problems from inception to working solutions independently.

Preferred Qualifications

• Experience in telecom, network operations, or other real-time complex systems.
• Background in applying ML to large-scale operational or sensor/time-series datasets.
• PhD or equivalent research expertise in machine learning, combined with practical application and delivery experience.
• Familiarity with large language models (LLMs) and agentic architectures as components within a broader ML toolkit.
• Contributions to published research or open-source projects demonstrating applied rigor.

Inclusion & Diversity

Zinkworks is dedicated to cultivating an inclusive and diverse workplace that values unique backgrounds and perspectives. The company actively supports initiatives that foster equity and opportunity both within the organization and in the broader community. This commitment enhances innovation and collaboration, reinforcing Zinkworks’ role as a trusted partner in the telecommunications and financial sectors worldwide.