AI Engineer

Modern Relay • Barcelona,

64d • Hybrid

About The Position

Modern Relay is building the knowledge platform for the agent era. Our product caters a new kind of company: one in which humans work alongside internal and external AI agents, and where coordination, context and trust become critical infrastructure. The platform provides a shared layer of truth where both humans and agents can propose updates, contribute knowledge and trigger workflows. This result in a living, compounding knowledge hub that can be read from, written to and improved by both people and software.

We’re looking for an AI Engineer to help build the data and model foundations that make Modern Relay’s platform reliable in production. You’ll work across data pipelines, model development, and ML infrastructure, turning messy signals into structured knowledge and high-quality model behavior. This role is ideal for someone who enjoys shipping end-to-end systems, from schema design and data infrastructure to training/evaluating models and improving them with feedback loops.

Requirements

• 0–6 years of experience in AI/ML engineering, data engineering, or a closely related role (we’re open to exceptional new grads with strong projects)
• Strong fundamentals in data engineering: pipelines, data modeling, schema design, and data quality practices
• Experience building or operating ML systems in production (training, evaluation, deployment, monitoring) or strong evidence you can ramp quickly
• Comfort working across the stack: from raw data and infrastructure to model behavior and product integration
• Familiarity with modern ML platforms and tooling (experiment tracking, dataset/versioning, orchestration, feature/data stores, model serving)
• Understanding of information theory concepts (e.g., entropy, mutual information) and how they relate to signal, compression, and evaluation)
• High ownership and a bias toward shipping: you can take ambiguous problems, propose a plan, and execute

Nice To Haves

• Experience with knowledge graphs or structured knowledge representations is a plus

Responsibilities

• Design and build data pipelines that ingest, clean, and transform product and customer data into high-signal training and evaluation datasets
• Own data infrastructure decisions (storage, orchestration, lineage, observability) to ensure reliability, scalability, and fast iteration
• Develop and improve ML/AI systems that power agent's behavior in task-solving, including retrieval, ranking, classification, and structured extraction
• Create and maintain schemas for agent memory, tool outputs, and conversation artifacts to make downstream modeling and analytics consistent
• Build evaluation harnesses and metrics to measure model quality, regressions, and real-world performance (offline + online)
• Work with knowledge representations (e.g., knowledge graphs) to connect entities, events, and business context for better reasoning and retrieval
• Partner closely with Product and Engineering to integrate models into production workflows with clear SLAs and monitoring
• Continuously improve feedback loops: labeling strategies, active learning, error analysis, and dataset/version management

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed