AI Platform Engineer, Backend Capabilities
Brain Co. · San Francisco Bay Area ·
- Work mode
- Hybrid
- Category
- Devops
- Experience
- 2+ years
- Visa sponsorship
- Yes
- Company size
- 51-200
Brain Co. · San Francisco Bay Area ·
Rebuild how the world works, to make institutions work better for the people they serve.
Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model.
Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.
Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.
You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now.
As an AI Platform Engineer at Brain Co., you will build the shared backend services and 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. You will own backend services from design through production, working closely with teams across Product, ML, and Infrastructure on system design and technical tradeoffs.
Design, build, and operate the platform backend services and data pipelines that power Brain Co.'s AI products. You will own the full lifecycle: from initial architecture and implementation to deployment and long-term maintenance.
Build the critical systems that accelerate our AI product development. This includes designing scalable solutions for ML experiment tracking, artifact management, and automated training and evaluation pipelines.
Engineer highly available, fault-tolerant systems with deep observability. Your architectures must be robust enough to meet the strict uptime and latency SLAs demanded by our enterprise and government clients.
Design modular and scalable architectures, clean APIs (REST, gRPC), and event-driven services with a long-term platform mindset. Continuously profile systems to optimize for latency, throughput, and costs.
Partner with Product, ML, Infrastructure, and customer-facing teams to build shared platform capabilities that remove bottlenecks and reduce the time it takes to ship new AI products to our clients.
You bring 2+ years of experience building and scaling production backend services or platforms, with strong proficiency in Python, Go, Rust, TypeScript, C++, or similar languages.
You understand what happens under the hood. You have strong fundamentals in distributed systems, including consistency, availability, failure modes, retries, and idempotency.
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 our internal ML and product teams as your primary customers. You have experience building shared infrastructure, internal platforms, or developer-facing services, with a focus on intuitive, well-documented APIs that help these teams build and ship effectively.
You have a track record of owning services with real uptime expectations. You design for observability from day one, using metrics, logging, and tracing, and take responsibility for incident response and on-call support for the systems you build.
You treat the platform as your own. You care about the end-to-end lifecycle of your systems and make pragmatic tradeoffs between immediate product needs and long-term platform health.
Experience with AI/ML platforms or inference systems, including integrating or managing ML experiment tracking, artifact management, and model registries (e.g., Weights & Biases, MLflow, ClearML) or orchestrators (e.g., Ray, Flyte, Kubeflow).
Experience designing and operating 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).
Familiarity with Kubernetes, cloud-native service deployment, or multi-tenant architectures.
Experience navigating strict compliance frameworks (e.g., SOC2, FedRAMP) or building systems for highly regulated, air-gapped, or on-premise environments.
Work alongside senior engineers from Tesla, DeepMind, Databricks, and other top engineering orgs.
Ship fast, learn constantly, and see your work protect production systems used by millions.
Earn competitive compensation and meaningful equity in a high-growth company.
Competitive salary plus equity
Daily lunches
Commuter benefits
401(k)
Medical, Dental, and Vision
Unlimited PTO