Modern Relay

Role Description 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. What You’ll Do - 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 Success Looks Like - 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 Qualifications - 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 - Experience with knowledge graphs or structured knowledge representations is a plus - High ownership and a bias toward shipping: you can take ambiguous problems, propose a plan, and execute Key Skills - 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 Why This Role - 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

Role Description We’re looking for a Principal Engineer to lead the design and delivery of core platform capabilities across our stack. You’ll operate as a senior technical leader—owning architecture decisions, driving execution on high-impact projects, and raising the bar for reliability, security, and developer velocity. This role is ideal for someone who thrives in ambiguity, can translate product goals into scalable systems, and enjoys mentoring engineers while staying hands-on. What You’ll Do - Own end-to-end architecture for key parts of the Modern Relay platform, from concept through production operations. - Design and implement scalable systems that power agentic collaboration, including orchestration, integrations, and data/knowledge layers. - Lead technical strategy for applying AI techniques (e.g., retrieval, evaluation, agent tooling) to deliver reliable, measurable product outcomes. - Build and evolve knowledge representations (namely knowledge graphs) to improve reasoning, personalization, and system observability. - Drive engineering excellence: testing strategy, performance tuning, reliability practices, security reviews, and incident learnings. - Partner closely with Product, Growth, and customer-facing teams to translate requirements into robust technical solutions. - Provide technical support and expertise in customer-facing contexts (troubleshooting, implementation support, and technical guidance). - Mentor and unblock engineers through design reviews, pairing, and setting clear technical standards. - Identify and execute on high-leverage improvements to developer experience, tooling, and platform foundations. - Perform other tasks and duties necessary for the proper fulfillment of the role and the Company’s business needs. What Success Looks Like - Core systems are measurably more reliable, secure, and scalable, with clear SLOs/SLAs and strong operational hygiene. - Architecture decisions are well-documented, pragmatic, and enable faster product iteration without sacrificing quality. - AI/knowledge features ship with strong evaluation, monitoring, and clear feedback loops to improve performance over time. - Cross-functional teams trust engineering as a partner—requirements are clarified early and delivered predictably. - Engineers are unblocked and growing through consistent technical leadership and high-quality reviews. Qualifications - 4–7 years of professional software engineering experience, including ownership of production systems. - Strong system architecture skills: designing distributed systems, APIs, data models, and integration patterns. - Experience building with or alongside AI systems in production (e.g., LLM applications, retrieval systems, evaluation/monitoring, agent frameworks). - Familiarity with knowledge graphs or graph-based modeling (or strong interest and ability to ramp quickly). - High technical bar: ability to write and review high-quality code, and to make sound tradeoffs under constraints. - Proven technical leadership: driving projects, aligning stakeholders, mentoring engineers, and setting standards. - Comfort engaging in customer-facing technical work when needed (debugging, implementation support, technical explanations). - Strong communication skills—able to explain complex systems clearly to both technical and non-technical audiences. Key Skills - System architecture - Artificial intelligence (production AI/LLM systems) - Knowledge graphs / graph modeling - Technical leadership - Cross-functional collaboration Why This Role - 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.