Member of Technical Staff, Backend Engineering

Radical Numerics•San Francisco, CA

About The Position

Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures for million-token context windows, enabling work toward AI-designed whole genomes and gene-editing tools. We believe the next generation of biological foundation models will require not only better models and training systems, but also robust backend infrastructure that makes those systems usable in practice. This role focuses on the backend services and APIs that connect our research platform to internal tools, external products, and real-world scientific workflows. As a Member of Technical Staff, Backend Engineering at Radical Numerics, you will design, build, and operate backend services that power APIs and platform capabilities across the company. You will help create the systems that make model capabilities accessible, reliable, and easy to integrate, whether for internal researchers, external users, or downstream scientific applications. This is a hands-on role for someone who wants to own backend systems end-to-end. You should be excited to move from API design to implementation to deployment to observability, while working closely with researchers and product-minded engineers to ensure the systems we build are useful, scalable, and maintainable.

Requirements

Strong track record building production backend systems, distributed systems, APIs, or data services.
Proficiency in at least one backend language such as Python, Go, Java, or Rust, along with strong software design fundamentals.
Experience designing and operating production APIs, including interface design, authentication, versioning, reliability, and monitoring.
Ability to own services end-to-end: architecture, implementation, testing, deployment, and operational support.
Strong understanding of scalable systems design, including data modeling, concurrency, failure modes, and performance tradeoffs.
Excellent written and verbal communication skills, especially the ability to collaborate across engineering, research, and scientific teams.

Nice To Haves

Experience with event-driven systems, streaming infrastructure, or workflow orchestration.
Experience with SQL, OLTP/OLAP systems, or data platforms that support analytics or model-facing applications.
Experience building self-serve internal platforms, multi-tenant services, or control-plane-style systems.
Familiarity with ML or AI product infrastructure, including telemetry, metadata services, inference-facing APIs, or evaluation-related backend systems.
Experience with security, governance, auditability, data retention, or privacy-aware backend design.
Background in distributed systems, infrastructure, computational biology, or another quantitative technical field.

Responsibilities

Build backend services for APIs and platform products.
Design and implement backend systems that expose model capabilities, data services, and internal platform functionality through clean, reliable APIs.
Own services end-to-end.
Take backend systems from design docs through implementation, testing, deployment, monitoring, and iteration based on real usage patterns and operational feedback.
Design for scalability and reliability.
Build services that can handle growing traffic, large datasets, and demanding internal workloads while maintaining correctness, low latency, and operational robustness.
Develop developer-friendly APIs and abstractions.
Create clear, well-documented interfaces that make it easy for internal teams and external users to build on top of our systems.
Improve backend architecture and platform foundations.
Analyze existing systems, identify bottlenecks, and improve the maintainability, fault tolerance, and self-serve usability of our backend stack.
Work closely with research and product teams.
Partner with model researchers, infrastructure engineers, and application teams to understand requirements and translate them into backend systems that support real workflows.
Build observability and operational tooling.
Develop logging, monitoring, tracing, and alerting systems that help us understand service behavior in production and respond quickly when things go wrong.