Platform Engineer

About Leaser AI

 Leaser AI helps multifamily asset owners turn exposed inventory into signed

 leases at the right economic cost.

 Our platform connects market, inventory, demand, prospect, leasing, and revenue

 signals. It uses that context to recommend and execute governed actions, measure

 the resulting business outcomes, and make the next decision stronger.

 We are building Decision Intelligence, not another dashboard, marketing

 automation tool, or generic AI assistant.

The role

 We are looking for a Platform Engineer who can strengthen the technical spine

 beneath Leaser's products.

 You will work across ingestion, identity and attribution, semantic metrics,

 decision services, governed execution, and the infrastructure that makes those

 capabilities reliable. This is a hands-on product engineering role with

 meaningful architectural ownership.

 You will be effective here if you enjoy moving between application code, data

 systems, infrastructure, and production operations. This is not a narrowly

 defined DevOps or SRE position.

 What you'll work on

 - Build reliable integrations with property-management systems, CRMs,

  advertising platforms, analytics systems, and other operational data sources.

 - Strengthen the identity and attribution spine connecting clicks, leads,

  prospects, tours, applications, and signed leases.

 - Develop canonical data models and metrics for exposure, occupancy, leasing

  velocity, acquisition cost, effective revenue, and related outcomes.

 - Build durable workflows for ingestion, backfills, OAuth refresh, write-backs,

  retries, reconciliation, and failure recovery.

 - Evolve the shared platform supporting Demand OS, Prospect OS, Revenue OS, and

  the Command Center.

 - Improve multi-tenant isolation, authorization, auditability, data lineage, and

  observability.

 - Build the governed action path from recommendation through approval,

  execution, receipt, rollback, and outcome evaluation.

 - Integrate AI models without allowing probabilistic behavior to redefine

  business truth, permissions, or safety boundaries.

 - Improve local development, testing, deployment, and production diagnostics

  across independently deployable services.

 - Make pragmatic architectural decisions appropriate for an ambitious early-

  stage platform.

 Our current stack

 - TypeScript and Node.js

 - NestJS services and APIs

 - Next.js and React

 - PostgreSQL and canonical SQL views

 - dbt analytics models

 - Docker and Docker Compose

 - AWS, Terraform, ECR, and related cloud infrastructure

 - Redis and Temporal-oriented workflow infrastructure

 - Anthropic models for analysis, explanation, and assisted workflows

 - A shared architecture spanning ingestion, attribution, metrics,

  recommendations, actions, and AI-assisted operating surfaces

 The repository currently behaves as a modular monolith over shared data,

 deployed as distinct services. We value engineers who can preserve coherent

 boundaries without introducing distributed-systems complexity before it is

 justified.

What we're looking for

 - Strong production experience with TypeScript, Node.js, and backend application

  development.

 - Fluency with PostgreSQL, relational modeling, SQL, migrations, and data

  integrity.

 - Experience designing and operating APIs, asynchronous jobs, integrations, or

  event-driven workflows.

 - Practical knowledge of AWS, containers, infrastructure as code, deployment

  automation, and production observability.

 - A strong instinct for idempotency, retries, reconciliation, audit trails, and

  failure handling.

 - The ability to work across service, data, and infrastructure boundaries

  without losing sight of the user outcome.

 - Clear technical judgment about what should be centralized, what should remain

  modular, and what should not be built yet.

 - Comfort working in an evolving codebase where you will improve foundations

  while continuing to ship product.

Especially valuable

 - Experience with dbt, BigQuery, Snowflake, or semantic metrics layers.

 - Experience with Temporal, Dagster, or another durable workflow system.

 - Work involving OAuth integrations, credential management, rate limits, schema

  drift, or large backfills.

 - Experience building multi-tenant enterprise software with row-level

  permissions and audit requirements.

 - Familiarity with identity resolution, attribution, experimentation, or causal

  measurement.

 - Experience building guarded AI or agentic systems that can recommend and

  execute real-world actions.

 - Knowledge of multifamily, property technology, advertising technology, or

  revenue-management systems.

 How we think about engineering

 - Business definitions should be computed once and consumed everywhere.

 - Every external action should be authorized, idempotent, observable, and

  auditable.

 - Missing or stale data must be visible rather than silently converted into

  certainty.

 - AI can interpret ambiguity; it cannot invent permissions, metrics, or business

  truth.

 - The best architecture is the simplest one that preserves trust and supports

  the next stage of growth.

 - Production outcomes matter more than architectural theater.

What success looks like

 Within your first several months, you will have:

 - Shipped meaningful improvements across the shared platform.

 - Made one or more critical ingestion or execution workflows materially more

  reliable.

 - Improved the consistency and traceability of data from source signal to

  signed-lease outcome.

 - Reduced the effort required to diagnose failures and operate the platform.

 - Helped establish engineering patterns that future product surfaces can reuse

  safely.

 - Developed enough product context to challenge requirements and recommend

  simpler, stronger approaches.

Why join

 You will help build the foundations of a new kind of enterprise system: one that

 does not merely automate work, but connects decisions to measurable outcomes and

 learns from what happens next.

 The problems are technically demanding, commercially meaningful, and still open

 enough for one strong engineer to shape how the platform develops.