Staff Software Engineer (Backend)Boston, USAbout UsAxiomatic 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.Our mission, 30×30, is to deliver a 30× improvement in the speed, accessibility, and cost of semiconductor and photonic hardware development by 2030.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.Position OverviewAs Staff Software Engineer (Backend), 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 hands-on, T-shaped role with a deep backend specialization: you'll spend roughly 70% of your time writing and reviewing backend code, 20% on architecture and technical strategy, and 10% contributing to frontend work when needed.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.You will:Write and ship backend code daily — this is first and foremost a hands-on engineering roleOwn the technical strategy for backend systems and AI infrastructureLead cross-functional initiatives spanning backend, AI, infra, and frontendDesign and evolve foundational platforms (model routing, agent runtime, persistence, observability)Drive engineering excellence through RFCs, standards, and architecture reviewsContribute to frontend development when needed, collaborating with frontend engineers on integration pointsMultiply the team through mentorship and force-multiplier code (frameworks, internal libraries, shared patterns)Be the technical owner of production reliability: incident response, performance, cost, securityKey Responsibilities1.
Technical Strategy & ArchitectureSet the 12–24 month technical roadmap for backend systems with the Head of Engineering / Lead Software EngineerAuthor RFCs and design documents that shape the engineering organizationMake build-vs-buy decisions on critical platform components (model routing, vector DBs, queues, eval pipelines)Design for scale, multi-tenancy, and compliance readinessDrive architecture reviews and ensure technical consistency across squads2. Platform & InfrastructureOwn foundational systems: conversation persistence, observability stack, and core platform servicesLead cost-optimization initiatives (caching strategies, batching, resource budgets)Establish SLOs and drive incident response, postmortems, and durable fixesPartner with infra on the deployment story (Cloud Run, Cloud SQL, VPCs, multi-region)Drive security and compliance (auth, secrets, data residency, audit trails)3. AI SystemsCollaborate with the AI team to integrate LLM-powered features into backend servicesDesign clean abstraction layers for model providers, enabling routing and fallbackContribute to patterns for prompt management, evaluation, and regression testingStay informed on emerging AI infrastructure trends and help evaluate build-vs-buy decisions4.
Engineering ExcellenceSet and enforce coding standards, review templates, and testing practicesDrive measurable quality improvements (p95 latency, error budgets, test coverage, infra cost)Identify systemic issues and design durable fixes, never one-off patchesBuild internal frameworks and libraries that raise the velocity of every other engineer5. Leadership & MentorshipMentor senior engineers and help them grow toward staffLead technical interviews and define the engineering barRepresent backend engineering in cross-functional planningCommunicate trade-offs clearly to product, leadership, and external stakeholdersCoach the team on debugging, performance work, and incident responseKey Requirements10+ years of backend development experience, with 2+ in a staff/principal/lead roleDocumented technical leadership: led architecture for multi-team systems, authored RFCs adopted org-wideDeep Python expertise: FastAPI, async, type system, profiling, internalsDistributed systems intuition: caching, queues, eventual consistency, idempotency, backpressureProduction-grade Databases: query optimization, schema migrations, partitioning, connection pooling, ORMs (SQLAlchemy)Cloud platform mastery: GCP (Cloud Run, Cloud SQL, GCS, VPCs, IAM, Auth0) designing, not just consumingComfort working alongside AI workloads: basic familiarity with LLM API integration patterns; willingness to learn and support AI infrastructure as neededSystems thinking: incident response, observability, SLO design, capacity planningForce-multiplier mindset: designed and shipped frameworks/libraries adopted by other engineersExcellent technical communication: RFCs, design docs, architecture reviews, async writingNice-to-HaveExperience with LLM integration in production (Anthropic, Google, OpenAI, Vertex AI)Familiarity with agent frameworks (Pydantic AI, LangGraph, FastMCP) or similarFrontend experience with React, Angular, or Vue — ability to contribute to UI when neededScaling an AI product from 0 ? 1 and 1 ?
10Authoring open source or internal frameworks adopted by other teamsPerformance-critical Python (Rust/Go interop, async tuning, native extensions)Multi-region / multi-tenant architectureSecurity/compliance background (SOC2, GDPR, secret management)Infrastructure as code (Terraform), GitOps, platform engineeringTech StackCurrent Stack:Backend: Python, FastAPI, SQLAlchemy, Pydantic AI, FastMCP, AlembicDatabases: PostgreSQL, Redis (caching)APIs: REST, WebSockets, SSE, MCPAI/ML: Anthropic Claude, Google Gemini, OpenAI, Vertex AI Model Garden, Mistral OCRCloud: Google Cloud Platform (Cloud Run, Cloud SQL, GCS, VPCs, Auth0)Infrastructure: Terraform, DockerCI/CD: GitHub ActionsObservability: Logfire, Sentry, OpenTelemetryTesting: pytest, pytest-asyncio, pytest-covWork model & location expectations:Team work model: Preferred hybrid from our Boston office; remote arrangement may be considered.Primary location: Boston, USWhy join us?At Axiomatic_AI, you will be working on technology that drives innovation in AI for scientific and engineering applications in line with our 30X30 mission.This is your opportunity to contribute to the development of new AI architectures that can reason coherently and produce interpret