AI Platform Engineer, Capabilities

As a core backend engineer at Brain Co., you will build the shared technical capabilities that help us scale our AI products quickly, safely, and reliably. You will take ambiguous product and technical challenges, turn them into clear system designs, and ship robust platforms that support real-world AI applications for the world’s most important institutions. If you are driven by high-stakes engineering and thrive on delivering systems built for uncompromising reliability, this is the exact opportunity for you.

Responsibilities

Design, build, and operate the platform backend services and data pipelines that power Brain Co.'s AI products; Build the critical systems that accelerate AI product development, including scalable solutions for ML experiment tracking, artifact management, and automated training and evaluation pipelines; Engineer highly available, fault-tolerant systems with deep observability; Design modular and scalable architectures and clean APIs (REST, gRPC) with a long-term platform mindset; Act as the bridge between engineering, product, and ML research.

Requirements

3+ years of experience building and scaling production backend services or platforms, ideally using languages common in the AI/Data ecosystem (e.g., Python, Go, Rust, or C++); You don't just use frameworks; you understand what happens under the hood. You have a deep grasp of consistency, availability, distributed failure modes, and idempotency; You treat our internal ML and product teams as your primary customers. You have a strong intuition for building intuitive, well-documented, and highly maintainable APIs and shared platforms; You have a track record of owning services with real uptime expectations. You design for observability from day one and aren't afraid of on-call responsibilities for the systems you build; You excel at breaking down complex, open-ended problems into clear technical designs, moving from first principles to production-ready systems with both speed and rigor; You treat the platform as your own. You care about the end-to-end lifecycle of your systems.

Nice to have

Integrated or managed standard ML experiment tracking, artifact management, and model registries (e.g., Weights & Biases, MLflow, ClearML) or orchestrators (e.g., Ray, Flyte, Kubeflow); Designed and operated high-throughput data pipelines and resilient asynchronous workflows using durable execution engines (e.g., Temporal), streaming platforms (e.g., Kafka), or distributed compute frameworks (e.g., Spark, Flink); Navigated strict compliance frameworks (e.g., SOC2, FedRAMP) or built systems specifically for highly regulated, air-gapped, or on-premise environments.

Benefits

Competitive salary plus equity; Daily lunches; Commuter benefits; 401(k); Medical, Dental, and Vision; Unlimited PTO