Lead AI Engineer
Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Aug 13, 2026.
About Andera
Andera is building reliable AI agents for back-office finance, starting with automating audits for the world's largest public companies.
Audits have resisted automation for decades due to the scale and instability of the underlying data. Large companies generate hundreds of millions of tokens of financial data across massive spreadsheets with constantly changing formats. Even understanding a single document can take expert auditors six or more hours, and a typical audit involves many of them.
We're building agents that can parse, interpret, and reason over data of this complexity-compressing gigabytes of messy financial information into representations that LLMs can work with reliably. The result is turning weeks of manual audit work into minutes of review.
This work requires long-horizon reasoning, robust data systems, and production-grade reliability-and it forms the foundation for automating some of the most complex financial workflows in the world.
Founding Team
Andera is built on the belief that this problem can only be solved by pairing elite auditors with elite engineers.
Engineering team: Alumni of MIT, Berkeley, and Waterloo, with experience at Jane Street, Stripe, Shopify, and Microsoft.
Audit team: Former #1 performers at Deloitte and leaders of national finance practices at Revolut.
The Role
We're hiring a Lead AI Engineer to set the technical direction for the agent systems at the heart of autonomous financial audits. You'll own architecture for long-horizon reasoning and large-scale data ingestion, raise the reliability bar, and grow the engineers building alongside you.
This is a high-ownership role with responsibility from architecture through deployment, and technical leadership across the agent stack.
What You'll Work On
Owning the architecture of our AI agent systems (planning, memory, tool use, recovery) and the roadmap to make them dependable
Setting the standard for evaluation, testing, and observability of agent behavior in production
Splitting and executing workloads that process ~100GB+ of data across processes and threads efficiently
Leading design reviews, mentoring engineers, and making the build-vs-buy and model-choice calls
Working directly with our audit experts to turn new workflows into shipped agent capabilities in hours, not weeks
What We're Looking For
Deep experience building complex agent or workflow-based systems - You can reason about how to rebuild systems like Claude-style agents from scratch, and you understand planning, tool use, memory, and failure modes.
Technical leadership track record - You've owned the architecture of a hard system and levelled up the engineers around you, without stepping back from writing code.
Strong fullstack and systems fundamentals - You ship fault-tolerant features end to end and design concurrent workloads over large datasets with throughput, backpressure, retries, and observability in mind.
Comfort learning non-engineering domains quickly - You can absorb audit concepts from domain experts and turn them into working systems fast.
Experience with or adjacent to our stack: TypeScript, Python, Temporal, Docker, Terraform, AWS Fargate