Founding Software Engineer — AI & Voice Systems

Role overview

A founding-level engineering position focused on building the evaluation and measurement infrastructure for an AI voice calling platform. The work centers on scoring production calls in near real time, layering audio-native signals (pronunciation, emotion, vocal stress) on top of LLM-based evaluators, and powering a simulation engine that places realistic automated calls. Getting this measurement layer right is treated as foundational to everything else in the product.

• Design and operate evaluation pipelines that score large volumes of production calls at low latency.
• Build audio-native metrics that go beyond transcripts, validating them against human-labeled ground truth alongside LLM-based evaluators.
• Extend a simulation engine that dials real agents across phone and WebRTC, modeling varied personas, accents, and background noise.
• Own large-scale analytics infrastructure, including columnar data stores, key-value streams, and the query layer feeding internal dashboards.
• Orchestrate long-running, distributed workflows (replays, backfills, load tests) using a durable workflow engine, with strong attention to idempotency and correctness.
• Operate what you build: dashboards, alerts, and root-cause investigation when production metrics drift.

• Four or more years of production experience building backend, ML, or audio systems, ideally in Type Script or Python.
• Hands-on depth in at least one of: speech/audio processing, LLM evaluation pipelines, or high-volume data infrastructure.
• Strong rigor around correctness, with comfort thinking in terms of idempotency, replay safety, and ground-truth labels rather than only happy paths.
• Willingness to own operations, including dashboards, alerts, and after-hours investigation.
• Clear written communication, with design documents and post-mortems people actually want to read.

• Familiarity with the listed stack (Type Script, Bun, AWS via SST, Click House, DynamoDB, Temporal, Postgres, and WebRTC/telephony integrations).
• Experience validating ML systems against human judgment at scale.