Axiomatic AI develops AI-powered verification infrastructure that automates and validates complex engineering and scientific workflows.
<h4><strong>About us: </strong></h4> <p>Axiomatic AI is building a new class of AI systems designed to reason with the rigor of the scientific method. By combining deep learning with formal logic and physics-based modeling, we create verifiable, interpretable AI systems that collaborate with and support human researchers in high-stakes scientific and engineering workflows. </p> <p>Our mission, 30×30, is to deliver a 30× improvement in the speed, accessibility, and cost of semiconductor and photonic hardware development by 2030. </p> <p>We aim to revolutionize hardware design and simulation in these industries and are building a team of highly motivated professionals to bring these innovations from research into commercial products.<br><br></p> <h4><strong>Position Overview</strong></h4> <p>As <strong>Staff Software Engineer (Backend)</strong>, you will set the technical direction for our backend platform and drive the systems that power an AI-native product at scale. This is a <strong>hands-on, T-shaped role with a deep backend specialization</strong>: you'll spend roughly <strong>70% of your time writing and reviewing backend code</strong>, <strong>20% on architecture and technical strategy</strong>, and <strong>10% contributing to frontend work</strong> when needed.</p> <p>You operate at the intersection of backend engineering, AI infrastructure, and platform reliability, making the foundational decisions that let every other engineer ship faster, safer, and cheaper.</p> <p>You will:</p> <ul> <li><strong>Write and ship backend code daily</strong> — this is first and foremost a hands-on engineering role</li> <li><strong>Own the technical strategy</strong> for backend systems and AI infrastructure</li> <li><strong>Lead cross-functional initiatives</strong> spanning backend, AI, infra, and frontend</li> <li><strong>Design and evolve foundational platforms</strong> (model routing, agent runtime, persistence, observability)</li> <li><strong>Drive engineering excellence</strong> through RFCs, standards, and architecture reviews</li> <li><strong>Contribute to frontend development</strong> when needed, collaborating with frontend engineers on integration points</li> <li><strong>Multiply the team</strong> through mentorship and force-multiplier code (frameworks, internal libraries, shared patterns)</li> <li><strong>Be the technical owner of production reliability</strong>: incident response, performance, cost, security</li> </ul> <h4><span class="notion-enable-hover" data-token-index="0">Key Responsibilities</span></h4> <h4><strong>1. Technical Strategy & Architecture</strong></h4> <ul> <li>Set the 12–24 month technical roadmap for backend systems with the Head of Engineering / Lead Software Engineer</li> <li>Author RFCs and design documents that shape the engineering organization</li> <li>Make build-vs-buy decisions on critical platform components (model routing, vector DBs, queues, eval pipelines)</li> <li>Design for scale, multi-tenancy, and compliance readiness</li> <li>Drive architecture reviews and ensure technical consistency across squads</li> </ul> <h4><strong>2. Platform & Infrastructure</strong></h4> <ul> <li>Own foundational systems: conversation persistence, observability stack, and core platform services</li> <li>Lead cost-optimization initiatives (caching strategies, batching, resource budgets)</li> <li>Establish SLOs and drive incident response, postmortems, and durable fixes</li> <li>Partner with infra on the deployment story (Cloud Run, Cloud SQL, VPCs, multi-region)</li> <li>Drive security and compliance (auth, secrets, data residency, audit trails)</li> </ul> <h4><strong>3. AI Systems</strong></h4> <ul> <li>Collaborate with the AI team to integrate LLM-powered features into backend services</li> <li>Design clean abstraction layers for model providers, enabling routing and fallback</li> <li>Contribute to patterns for prompt management, evaluation, and regression testing</li> <li>Stay informed on emerging AI infrastructure trends and help evaluate build-vs-buy decisions</li> </ul> <h4><strong>4. Engineering Excellence</strong></h4> <ul> <li>Set and enforce coding standards, review templates, and testing practices</li> <li>Drive measurable quality improvements (p95 latency, error budgets, test coverage, infra cost)</li> <li>Identify systemic issues and design durable fixes, never one-off patches</li> <li>Build internal frameworks and libraries that raise the velocity of every other engineer</li> </ul> <h4><strong>5. Leadership & Mentorship</strong></h4> <ul> <li>Mentor senior engineers and help them grow toward staff</li> <li>Lead technical interviews and define the engineering bar</li> <li>Represent backend engineering in cross-functional planning</li> <li>Communicate trade-offs clearly to product, leadership, and external stakeholders</li> <li>Coach the team on debugging, performance work, and incident response</li> </ul> <h4><strong>Key Requirements </strong></h4> <ul> <li><strong>10+ years of backend development experience</strong>, with 2+ in a staff/principal/lead role</li> <li><strong>Documented technical leadership</strong>: led architecture for multi-team systems, authored RFCs adopted org-wide</li> <li><strong>Deep Python expertise</strong>: FastAPI, async, type system, profiling, internals</li> <li><strong>Distributed systems intuition</strong>: caching, queues, eventual consistency, idempotency, backpressure</li> <li><strong>Production-grade Databases</strong>: query optimization, schema migrations, partitioning, connection pooling, ORMs (SQLAlchemy)</li> <li><strong>Cloud platform mastery</strong>: GCP (Cloud Run, Cloud SQL, GCS, VPCs, IAM, Auth0) designing, not just consuming</li> <li><strong>Comfort working alongside AI workloads</strong>: basic familiarity with LLM API integration patterns; willingness to learn and support AI infrastructure as needed</li> <li><strong>Systems thinking</strong>: incident response, observability