Job Description

**About Kernel**

Kernel Intelligence is building the intelligence layer for commercial real
estate (CRE) by turning unstructured documents—like leases, invoices,
contracts, and diligence reports—into high-quality, decision-ready data. CRE
is a $20T+ asset class, yet much of its critical information still lives in
PDFs and siloed systems. We’re fixing that with a modern, ML-powered platform
designed for high-stakes, data-heavy workflows.

Our team combines deep industry experience (we previously built the leading
real-estate compliance platform) with strong distributed-systems and AI
infrastructure expertise. We’re building the data foundation that will power
the next generation of CRE intelligence.

**About the Team**

We’re a small, senior team of builders who value clarity, curiosity, and
decisive execution—shipping fast without sacrificing correctness or
reliability.

We are an AI-enabled data platform for mid-market companies who value
enterprise-grade (multi-tenant) systems that are scalable, secure, reliable,
predictable, and accurate.

You’ll work directly with experienced engineers on well-defined, high-impact
backend systems that form the foundation for next-generation AI and data
products. Unlike many startups, “build fast and break things” is our anti-
motto! And unlike many large companies, every line of code that every
developer contributes has a meaningful impact on our business.

**Tech Snapshot**

* **Python 3.12+** , AsyncIO
* **FastAPI** / Starlette
* **SQLAlchemy 2.x (async)**
* **Pulsar or Kafka** (typed events, producers/consumers)
* **OIDC/JWT** , multi-tenant auth
* **Kubernetes** , cloud-native services
* **GitOps/Terraform** , CI/CD
* **OpenTelemetry** , Prometheus, structured logs
* Distributed systems fundamentals: idempotency, retries, backpressure, consistency

**About the Role**

As a Backend Systems Engineer, you’ll design and build the distributed systems
that power our AI-driven platform. You’ll work directly with our CTO to
architect backend services, evolve core data pipelines, harden multi-tenant
authentication, and scale integrations between internal and customer systems.

This is an engineering-led, application-development-focused systems role —
ideal for someone who enjoys building robust services that make real AI
workloads work reliably at scale.

**Responsibilities**

* Design and build distributed backend services in Python (3.12+) using FastAPI and modern async patterns
* Implement event-driven pipelines (typed events, queues, idempotent handlers) that are observable and resilient
* Develop secure, multi-tenant authentication/authorization with OIDC/JWT and policy-driven access control
* Model and evolve data using SQLAlchemy 2.x (async) with strong transactional guarantees
* Apply dependency injection patterns (e.g., dependency_injector or equivalent) for modular, testable systems
* Instrument everything — traces, metrics, structured logs — and use real runtime data to drive improvements
* Deploy and operate services on Kubernetes with platform engineers (GitOps/Terraform collaboration)
* Drive reliability through rigorous testing (unit, async integration, and functional tests)

**Example Projects**

* Add new FastAPI endpoints secured by OIDC scopes with full request-context propagation
* Build typed producers/consumers over Pulsar or Kafka to support new distributed workflows
* Implement distributed idempotency and leader election for singleton tasks and critical operations
* Extend DI-based resource providers with auto-instrumentation hooks and namespace replication
* Tune async SQLAlchemy transaction performance and manage connection lifecycles at scale
* Define SLO-adjacent telemetry and use traces/metrics to uncover and resolve performance bottlenecks

**What You Bring**

* 5+ years of experience building backend or distributed systems
* Strong fluency in Python 3.10+ with async/await (we run 3.12)
* Deep experience with FastAPI or Starlette (middleware, request context, router composition)
* Hands-on skill with SQLAlchemy 2.x async engines, sessions, and repository patterns
* Solid understanding of distributed-systems fundamentals: retries, backpressure, idempotency, consistency
* Practical experience integrating OIDC/JWT in multi-tenant applications
* Comfort with dependency injection frameworks/patterns
* An observability-first mindset — you instrument before you guess
* Experience building cloud-native services on Kubernetes

**Bonus Points For**

* Production experience with Pulsar or Kafka (typed schemas, batching, QoS tuning)
* Experience with Zitadel or other modern IdPs
* Familiarity with multi-tenant runtime schemas or dynamic data isolation
* Experience with AI-integration patterns (LiteLLM, retrieval, preprocessing)
* GitOps (Argo CD) and CI/CD (GitLab) workflows
* Security-first practices: secrets hygiene, auditable logging, least-privilege design
* Comfortable using AI-assisted development tools
* Experience with enterprise SaaS systems

**Mindset**

* You take ownership and deliver — you ship, measure, iterate
* You communicate clearly and collaborate well across engineering and product
* You favor clarity, instrumentation, and reliability over ceremony
* You enjoy solving foundational problems in complex systems

**Why Join Us**

* Work closely with a highly technical founding team that values design clarity and data-driven engineering
* Build core systems that power AI-enabled data products in a massive, under-modernized industry
* Have direct, visible impact in an early-stage environment
* Flexible remote role with competitive compensation