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As an AI Platform Backend Engineer, you will own and build the core backend systems that power Brain Co.’s AI platform. This is a foundational, high-leverage role: your work enables product teams, ML engineers, and customer deployments to move faster and more safely. You will design and operate backend services that support complex AI workflows (multi-step reasoning, tool use, human-in-the-loop processes), with a strong focus on scalability, reliability, and developer experience. You’ll take systems from early technical exploration through production deployment, often in ambiguous problem spaces where you help define the right solution. This role offers significant autonomy and the opportunity to grow into a broader technical and organizational leadership role over time.
Job Responsibility:
Design, build, and operate foundational backend services and data pipelines that power Brain Co.’s AI platform
Take end-to-end ownership: architecture, implementation, deployment, and long-term maintenance
Build systems with strong guarantees around correctness, fault tolerance, and observability
Design for real uptime expectations in enterprise and government environments
Design modular, reusable backend architectures and APIs (REST, gRPC, event-driven)
Make sound architectural tradeoffs with a long-term platform mindset
Break down open-ended, complex problems into clear technical designs
Move from first principles to production-ready systems with speed and rigor
Improve latency, throughput, and cost efficiency through profiling, thoughtful system design, and iteration
Work closely with Product, ML, Infrastructure, and customer-facing teams
Enable others by building systems that are intuitive, reliable, and developer-friendly
Requirements:
5+ years of experience building backend systems or platforms in production
Strong fundamentals in distributed systems (consistency, availability, failure modes, retries, idempotency)
Deep proficiency in at least one backend language (Go, Typescript, Rust, Python, or similar)
Experience designing, operating, and evolving APIs and services at scale
Proven ability to design systems from first principles
Experience building shared infrastructure, internal platforms, or developer-facing services
Strong intuition for developer experience and long-term maintainability
Experience owning services with real uptime and operational responsibility
Familiarity with observability tooling (metrics, logging, tracing) and incident response
Nice to have:
Experience with AI/ML platforms, inference systems, or data-intensive pipelines
Familiarity with Kubernetes and cloud-native service deployment
Exposure to multi-tenant, regulated, or government environments
Experience with real-time or streaming data systems