DimRed is building a platform to automate the most challenging parts of scaling AI Agents and LLM-based systems. We believe the path forward for AI systems is grounded in reliability and interpretability. Our platform leverages first principles-based thinking and ML theory to automate the manual process of iterating on prompts, LLM evaluations, and agent experimentation.
Our mission is to enable anyone to easily automate knowledge work with LLMs through task demonstration.
We are looking for a Founding Engineer, Backend who will be critical to the design and development of highly scalable, data-intensive AI systems. You’ll partner closely with the CEO and research scientists to bridge the gap between research and production, and your work will directly shape the foundation of DimRed's platform.
Design and implement core backend services that power large-scale evaluations, agent workflows, and data pipelines.
Own orchestration for long-running workflows (hours → days) with durable execution, retries, and robust state management.
Build and productionize vector search infrastructure (e.g., pgvector, Pinecone) to support retrieval-augmented generation and agent memory.
Establish secure multi-tenant foundations (organizations, API keys, RBAC, audit logs) aligned with enterprise and SOC2 requirements.
Work directly with customers to understand integration challenges and translate them into backend features and APIs.
2–4 years of experience as a software engineer building data-intensive backends (startup experience preferred).
Strong skills in Python (FastAPI, asyncio), with clean, maintainable coding practices.
Familiarity with Kubernetes, Docker, and AWS services (EKS/ECS, RDS/Aurora, S3, IAM).
Hands-on experience with async and event-driven systems (queues/streams like SQS, Kafka, or EventBridge).
Knowledge of vector databases (pgvector, Pinecone, Weaviate, etc.).
Strong understanding of security best practices, including RBAC, least-privilege IAM, and data encryption.
Comfort with observability (metrics, tracing, logging) and debugging complex distributed systems.
Ability to thrive in an early-stage startup environment: ambiguity, rapid iteration, and direct customer feedback loops.
Competitive salary and top-of-market equity compensation.
Flexible PTO policy.
Professional development budget.
AI tooling budget.
Opportunity to shape the direction of a fast-growing startup.
As one of the first engineers, you’ll shape our architecture, culture, and engineering practices. You’ll work at the intersection of AI research and production systems—turning ideas into infrastructure that scales. If you want to help define how reliable and interpretable AI agents are built, we’d love to talk.
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