AI Engineer

• Build core AI infrastructure that directly impacts product reliability and customer outcomes

• Work on real-world agent coordination problems where data quality, structure, and evaluation matter as much as models

• High autonomy and ownership in a fast-moving team shipping at the frontier of applied AI

• A chance to define how Modern Relay's agents learn from data and improve over time

• 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

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

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.

• 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

• San Francisco, CA

• New York City, NY

• Barcelona, Spain

• Remote (U.S. and Europe)

• Data pipelines and datasets are trustworthy, well-instrumented, and easy to iterate on as product needs evolve

• Model performance improves measurably over time with clear evaluation methodology and fast debugging cycles

• Agent outputs become more consistent and structured through strong schema design and robust post-processing/validation

• Knowledge and retrieval systems reduce hallucinations and increase task completion rates in real customer workflows

• Cross-functional teams can confidently ship AI improvements because quality, monitoring, and rollback paths are in place

• Data pipelines

• Data engineering and data infrastructure

• AI / artificial intelligence

• Machine learning platforms and production ML

• Model development, evaluation, and monitoring

• Schema design and structured data systems

• Knowledge graphs and information retrieval

• Information theory fundamentals