Member of Technical Staff - Backend/API Platform

RADIXARK, INC.
Palo Alto, CA, United States
2 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
4 years minimum
Compensation
$200,000
Working hours
Regular working hours

Tech stack

Admin Tools Application Programming Interfaces (APIs) Amazon Web Services Systems Engineering User Authentication Microsoft Azure Cloud Computing Databases Software Debugging Distributed Systems Monitoring of Systems Identity and Access Management
+26 more
Job Scheduling Python (Programming Language) Network Control Queueing Systems Prometheus Management of Software Versions Datadog Data Logging Scripting Google Cloud Grafana Caching Backend Rate Limiting Usage Tracking Build Management Kubernetes Information Technology Deployment Automation Production Code SGLang Machine Learning Operations Restful APIs Api Management Golang Programming Languages

Job description

RadixArk is looking for a Member of Technical Staff - Backend/API Platform Engineer to build the API layer, control plane, and platform services that power SGLang and Miles in production. You’ll design and implement the REST/gRPC APIs, authentication systems, multi-tenancy isolation, and monitoring infrastructure that thousands of developers and companies rely on. This role bridges high-performance inference/training systems with production-grade platform engineering., * Design and build production APIs for SGLang and Miles: REST/gRPC endpoints, client SDKs, API versioning

  • Implement authentication, authorization, and rate limiting systems for multi-tenant deployments
  • Build control plane infrastructure: job scheduling, resource allocation, model deployment management
  • Create monitoring, logging, and observability systems for production inference and training workloads
  • Design and implement billing integration, usage tracking, and quota management
  • Build management dashboards and admin tools for cluster operations
  • Ensure API reliability, performance, and security at scale
  • Implement multi-tenancy isolation and security boundaries
  • Create deployment automation, CI/CD pipelines, and rollback procedures
  • Write comprehensive API documentation and integration guides
  • Partner with Systems Engineers to optimize end-to-end latency from API serving layer
  • Debug production issues and implement reliability improvements

Requirements

Application Programming Interface (API), Authentication, Application Programming Interface (API), REST (Representational State Transfer), Production Systems, Computer Science, Python Programming/Scripting Language, Go Programming Language (Golang), Quality Metrics, Distributed Computing, * 4+ years experience building production backend systems, APIs, or platform infrastructure

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent industry experience
  • Strong proficiency in Python, Go, or Rust with production-quality code standards
  • Experience designing and building REST/gRPC APIs at scale
  • Solid understanding of distributed systems, databases, caching, and message queues
  • Experience with authentication, authorization, rate limiting, and multi-tenancy
  • Familiarity with cloud platforms (AWS, GCP, Azure) and Kubernetes
  • Experience with monitoring and observability tools (Prometheus, Grafana, DataDog)
  • Understanding of ML serving infrastructure or high-throughput systems is a plus

Benefits & conditions

Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.

About the company

RadixArk is an infrastructure-first company built by engineers who’ve shipped production AI systems, created SGLang (30K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework). Founded by AI infrastructure veterans from xAI and NVIDIA, we’re on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training. Our team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating 10,000+ GPUs, and contributed to infrastructure that powers leading AI companies and research labs.

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