Founding Platform Engineer

Founding Platform Engineer
Early-stage AI company building conversation intelligence for enterprise contact centers

The Role
You’ll be the first senior engineer on the team and the architectural counterpart to the CTO. You’ll write code regularly, own the hardest parts of the platform, and set the bar for the engineers who build alongside you.

What You’ll Own

Conversation pipeline and integrations, to start. Own the normalization, segmentation, and enrichment that turn raw conversations into structured, scorable evidence, including the audio and transcription path as voice deepens from transcript ingestion toward raw calls. Build the first contact-center, ticketing, and direct-upload integrations and the reusable framework a dedicated integrations engineer will later extend.
Provenance and auditability. Build immutable versioning for rubrics and scoring configuration, preserve evidence through the transcript lifecycle, create tamper-evident audit trails, and make it possible to answer what an interaction scored eight months ago and why.
The multi-tenant platform. Deliver tenant isolation from the infrastructure level down, per-customer configuration and entitlements, and the roles and permissions enterprise data separation requires.
The engineering platform. Build the paved road: infrastructure as code, CI/CD, environment management, logging, metrics, tracing, alerting, secrets management, backups, disaster recovery, and incident response. The company is not hiring a dedicated DevOps or SRE engineer; every engineer owns the production behavior of what they ship.
Data and reliability. Own storage and retention for conversation data at volume, queueing, idempotent reprocessing that never multiplies a customer’s inference spend, on-call, SLOs, and a release process that doesn’t depend on heroics. The path from pilot scale to millions of interactions a month is a real platform problem, and closing it is part of the job.
Security and compliance in the codebase. Own sensitive-data handling and redaction before any model sees a conversation, access control, evidence export, and the engineering half of SOC 2 and the enterprise security reviews that gate each deal.

What We’re Looking For

Production software ownership at staff or principal scope. You’ve owned systems other teams depended on, set technical direction others followed, and carried the operational burden for them.
Strong distributed-systems and backend depth, with experience in high-volume data or event-processing systems, and strong API and data-model design.
You can own infrastructure without a dedicated DevOps team. You’ve built the IaC, CI/CD, and observability layer yourself rather than inherited it from a platform org.
Startup experience. You’ve shipped at an early-stage or high-growth company with broad ownership, ambiguity, and no support functions to fall back on. This is a requirement, not a preference.
Depth in Python, and enough TypeScript and React to work in the portal. You can trace a problem from the data model through the API to the screen, and make the frontend change yourself when it’s small.
Conversant across the ML pipeline. You can reason about how models are trained, evaluated, deployed, and monitored, and hold a substantive design conversation with the ML Lead about accuracy, latency, and cost. Research experience is not required, and this role does not involve research work.
Multi-tenant SaaS at enterprise scale, including tenant isolation and per-customer entitlements.
You get useful leverage from coding agents (the team ships with Claude Code daily) and can hold a quality line on what they produce.

Bonus

Audit, provenance, or compliance-grade data systems, where “prove this record is what it was” was a product requirement.
Speech and audio in production: ASR, diarization, streaming transcription, or real-time latency budgets.
Contact center, CCaaS, QA and workforce optimization, or compliance and GRC domain experience.