Staff Software Engineer (Backend)

Axiomatic_AI•Boston, MA

2h•Hybrid

About The Position

Axiomatic AI is building AI systems that combine deep learning with formal logic and physics-based modeling to support human researchers in scientific and engineering workflows. Its mission is to improve the speed, accessibility, and cost of semiconductor and photonic hardware development by 30× by 2030. The Staff Software Engineer (Backend) will set technical direction for the backend platform and build systems that power an AI-native product. This is a hands-on, backend-specialized role: approximately 70% backend coding and code review, 20% architecture and technical strategy, and 10% frontend work as needed. The engineer works across backend engineering, AI infrastructure, and platform reliability, making foundational decisions that help other engineers ship faster, more safely, and more cost-effectively. The role includes owning backend and AI infrastructure strategy; designing foundational systems such as model routing, agent runtime, persistence, and observability; leading cross-functional initiatives; and supporting production reliability across incident response, performance, cost, and security. The position is hybrid and based in Boston, Massachusetts.

Requirements

10+ years of backend development experience, including 2+ years in a staff, principal, or lead role.
Documented technical leadership, including leading architecture for multi-team systems and authoring RFCs adopted organization-wide.
Deep Python expertise, including FastAPI, asynchronous programming, the type system, profiling, and Python internals.
Distributed-systems knowledge, including caching, queues, eventual consistency, idempotency, and backpressure.
Production-grade database expertise, including query optimization, schema migrations, partitioning, connection pooling, and ORMs such as SQLAlchemy.
Cloud platform mastery with GCP, including designing with Cloud Run, Cloud SQL, GCS, VPCs, IAM, and Auth0.
Basic familiarity with LLM API integration patterns and willingness to learn and support AI infrastructure as needed.
Systems thinking across incident response, observability, SLO design, and capacity planning.
Experience designing and shipping frameworks or libraries adopted by other engineers.
Excellent technical communication skills, including RFCs, design documents, architecture reviews, and asynchronous writing.

Nice To Haves

Production experience integrating LLMs, such as Anthropic, Google, OpenAI, or Vertex AI.
Familiarity with agent frameworks such as Pydantic AI, LangGraph, or FastMCP, or similar tools.
Frontend experience with React, Angular, or Vue, with the ability to contribute to UI work as needed.
Experience scaling an AI product from 0 to 1 and from 1 to 10.
Experience authoring open-source or internal frameworks adopted by other teams.
Experience with performance-critical Python, Rust or Go interoperation, asynchronous tuning, or native extensions.
Experience with multi-region or multi-tenant architecture.
Security or compliance experience, including SOC 2, GDPR, or secret management.
Experience with infrastructure as code, Terraform, GitOps, or platform engineering.

Responsibilities

Write and ship backend code daily and review backend code.
Set the technical strategy and 12–24 month roadmap for backend systems and AI infrastructure in collaboration with the Head of Engineering / Lead Software Engineer.
Author RFCs and design documents, and drive architecture reviews and technical consistency across teams.
Make build-versus-buy decisions for platform components such as model routing, vector databases, queues, and evaluation pipelines.
Design systems for scale, multi-tenancy, and compliance readiness.
Own foundational systems, including conversation persistence, observability, and core platform services.
Lead cost-optimization initiatives, including caching, batching, and resource budgets.
Establish SLOs; lead incident response and postmortems; and implement durable fixes.
Partner with infrastructure teams on deployment architecture involving Cloud Run, Cloud SQL, VPCs, and multi-region systems.
Drive security and compliance work involving authentication, secrets, data residency, and audit trails.
Collaborate with the AI team to integrate LLM-powered features into backend services.
Design model-provider abstraction layers that support routing and fallback, and contribute to prompt management, evaluation, and regression-testing patterns.
Set coding standards, review templates, and testing practices; improve latency, error budgets, test coverage, and infrastructure costs.
Build internal frameworks and libraries that improve engineering velocity.
Mentor senior engineers, conduct technical interviews, and help define the engineering bar.
Represent backend engineering in cross-functional planning and communicate technical trade-offs to product, leadership, and external stakeholders.
Contribute to frontend development when needed and collaborate with frontend engineers on integration points.
Coach the team on debugging, performance work, and incident response.