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Staff Software Engineer (Backend)
About us:
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.
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 Overview
As 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 role
- Own the technical strategy for backend systems and AI infrastructure
- Lead cross-functional initiatives spanning backend, AI, infra, and frontend
- Design and evolve foundational platforms (model routing, agent runtime, persistence, observability)
- Drive engineering excellence through RFCs, standards, and architecture reviews
- Contribute to frontend development when needed, collaborating with frontend engineers on integration points
- Multiply the team through mentorship and force-multiplier code (frameworks, internal libraries, shared patterns)
- Be the technical owner of production reliability: incident response, performance, cost, security
Key Responsibilities
1. Technical Strategy & Architecture
- Set the 12–24 month technical roadmap for backend systems with the Head of Engineering / Lead Software Engineer
- Author RFCs and design documents that shape the engineering organization
- Make build-vs-buy decisions on critical platform components (model routing, vector DBs, queues, eval pipelines)
- Design for scale, multi-tenancy, and compliance readiness
- Drive architecture reviews and ensure technical consistency across squads
2. Platform & Infrastructure
- Own foundational systems: conversation persistence, observability stack, and core platform services
- Lead cost-optimization initiatives (caching strategies, batching, resource budgets)
- Establish SLOs and drive incident response, postmortems, and durable fixes
- Partner 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 Systems
- Collaborate with the AI team to integrate LLM-powered features into backend services
- Design clean abstraction layers for model providers, enabling routing and fallback
- Contribute to patterns for prompt management, evaluation, and regression testing
- Stay informed on emerging AI infrastructure trends and help evaluate build-vs-buy decisions
4. Engineering Excellence
- Set and enforce coding standards, review templates, and testing practices
- Drive measurable quality improvements (p95 latency, error budgets, test coverage, infra cost)
- Identify systemic issues and design durable fixes, never one-off patches
- Build internal frameworks and libraries that raise the velocity of every other engineer
5. Leadership & Mentorship
- Mentor senior engineers and help them grow toward staff
- Lead technical interviews and define the engineering bar
- Represent backend engineering in cross-functional planning
- Communicate trade-offs clearly to product, leadership, and external stakeholders
- Coach the team on debugging, performance work, and incident response
Key Requirements
- 10+ years of backend development experience, with 2+ in a staff/principal/lead role
- Documented technical leadership: led architecture for multi-team systems, authored RFCs adopted org-wide
- Deep Python expertise: FastAPI, async, type system, profiling, internals
- Distributed systems intuition: caching, queues, eventual consistency, idempotency, backpressure
- Production-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 consuming
- Comfort working alongside AI workloads: basic familiarity with LLM API integration patterns; willingness to learn and support AI infrastructure as needed
- Systems thinking: incident response, observability, SLO design, capacity planning
- Force-multiplier mindset: designed and shipped frameworks/libraries adopted by other engineers
- Excellent technical communication: RFCs, design docs, architecture reviews, async writing
Nice-to-Have
- Experience with LLM integration in production (Anthropic, Google, OpenAI, Vertex AI)
- Familiarity with agent frameworks (Pydantic AI, LangGraph, FastMCP) or similar
- Frontend experience with React, Angular, or Vue — ability to contribute to UI when needed
- Scaling an AI product from 0 1 and 1 10
- Authoring open source or internal frameworks adopted by other teams
- Performance-critical Python (Rust/Go interop, async tuning, native extensions)
- Multi-region / multi-tenant architecture
- Security/compliance background (SOC2, GDPR, secret management)
- Infrastructure as code (Terraform), GitOps, platform engineering
Tech Stack
Current Stack:
- Backend: Python, FastAPI, SQLAlchemy, Pydantic AI, FastMCP, Alembic
- Databases: PostgreSQL, Redis (caching)
- APIs: REST, WebSockets, SSE, MCP
- AI/ML: Anthropic Claude, Google Gemini, OpenAI, Vertex AI Model Garden, Mistral OCR
- Cloud: Google Cloud Platform (Cloud Run, Cloud SQL, GCS, VPCs, Auth0)
- Infrastructure: Terraform, Docker
- CI/CD: GitHub Actions
- Observability: Logfire, Sentry, OpenTelemetry
- Testing: pytest, pytest-asyncio, pytest-cov
Work model & location expectations:
-
Team work model:
Preferred hybrid from our Boston office; remote arrangement may be considered.
-
Primary location:
Boston, US
Why 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 interpretable and verifiable solutions. Consequently, see those ideas commercialized into products that will shape the future of hardware and computing, while collaborating with a global team of engineers and AI specialists.
We believe in pushing the boundaries of what is possible and continuously seek to redefine the intersection of AI, with focus on formal consistency. If you're ready to take your expertise in artificial intelligence and physics to the next level, we want to hear from you!
Worried about not meeting every qualification? Studies show that women and people of color are less likely to apply for jobs unless they meet every listed requirement. At Axiomatic-AI, we are dedicated to creating a diverse, inclusive, and authentic workplace. If this role excites you but your background doesn't perfectly match every qualification, we still encourage you to apply. You could be the perfect fit for this position or another opportunity with us.
