
AI Platform Engineer, Backend Capabilities
Primary stack
Nice to have's
Job description
About the Position
AI Platform Engineer, Backend Capabilities
San Francisco Bay Area
About the Role
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.
Responsibilities
- 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.
Requirements
- 2+ years of experience building and scaling production backend services or platforms, with strong proficiency in Python, Go, Rust, TypeScript, C++, or similar languages.
- Strong fundamentals in distributed systems, including consistency, availability, failure modes, retries, and idempotency.
- Ability to break down complex, open-ended problems into clear technical designs, moving from first principles to production-ready systems with both speed and rigor.
- Experience building shared infrastructure, internal platforms, or developer-facing services, with a focus on intuitive, well-documented APIs that help teams build and ship effectively.
- Track record of owning services with real uptime expectations. 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.
- Treat the platform as your own. Care about the end-to-end lifecycle of your systems and make pragmatic tradeoffs between immediate product needs and long-term platform health.
Nice to Have
- 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.
We Offer
- 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.
- Competitive compensation and meaningful equity in a high-growth company.
Benefits
- Competitive salary plus equity
- Daily lunches
- Commuter benefits
- 401(k)
- Medical, Dental, and Vision
- Unlimited PTO
About the Company
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.
© Label Your Data. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.
Label Your Data provides secure and high quality data annotation services for Computer Vision or NLP applications since 2020. We help Data Scientists and AI Engineers streamline the dataset labeling and focus on model development.About Label Your Data:— Founded in 2020;— 200+ annotation experts in Europe, LATAM and Africa;— 98%+ annotation accuracy benchmark;— 55 supported languages;— PCI/DSS certified;— ISO/IEC 27001:2013 certified;— GDPR, CCPA and HIPAA-compliant;— HQs in North America, EU and Asia.Our experience spans over 20 industries including Automotive, Robotics, Agriculture, E-commerce, Retail, Healthcare, Manufacturing, Fintech and Insurance.Our core services include but are not limited to:— Image & video annotation: 2D boxes, OCR, object/action detection, polygons, key points, semantic segmentation, 3D cuboids;— Text annotation: classification, NER, intent & sentiment analysis, prompts creation, Q&A pairs generation;— Audio annotation: transcription, sentiment recognition;— Sensor data annotation;— Data classification & categorization;— Data entry;— Data collection;— Model validation.
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