Senior Applied AI Engineer

Firmable is the market-leading B2B sales intelligence platform in Asia Pacific, backed by leading investors, growing 2,000+ customers strong and growing in the US. We build one of the richest datasets about businesses anywhere: who they are, who works there, and what is changing. Millions of companies, hundreds of sources, resolved into a single record and served to humans and AI agents alike.

This role sits on a small senior team working on extraction and structuring at global scale. Turning messy, inconsistent records from many sources into accurate facts about 15M companies and 130M people. The accuracy bar is high and the cost bar is low, because both are measured per record and there are billions of them.

As an Applied AI Engineer on this team, you build the multi-step agent systems that bring new data in and turn raw records into structured, verifiable facts.

That means agents that plan across several steps, call tools, hold state, and stop for human approval before anything is committed. It also means the extraction underneath: getting reliable structured output from HTML, undocumented APIs, protocol banners and free text, where the target set is not known in advance and the source is under no obligation to be consistent.

This is a build role. You will prototype fast, then be the person who makes the prototype hold at volume.

Expect to invent. The published approaches to extraction and agent reliability were not written for this volume or this accuracy bar, and you will find where they stop short. Designing the replacement method, and proving it works, is the job rather than a side project.

• Build agents that plan across many steps, call tools, hold state between them, and stop for human approval before anything is trusted

• Make every step observable and independently testable, with retries, idempotency and hard budgets on cost, latency and step count

• Handle partial failure properly. A run that dies two thirds of the way through a long job should not cost you the whole job

• Set the reliability bar for agents doing unattended work at volume, and own it when it slips

• Get reliable structured output from HTML, PDF, undocumented JSON APIs, protocol banners and free text, using constrained decoding, JSON schema enforcement, function calling and Pydantic validation

• Extract against an open vocabulary, where the target set is not fixed in advance. Regex and fixed signature lists are the baseline to beat, not the approach

• Push accuracy on sources that are inconsistent, incomplete and occasionally hostile to being parsed

• Design new methods where the existing ones fall short, and produce the evaluation that proves the new one is better

• Own the path from extracted entities and relationships into the graph, mapping them onto the schema, removing duplicates, and carrying confidence through instead of dropping it at the boundary

• Have models propose schema-constrained changes rather than write freely, with provenance attached to every fact

• Build the review path, so a human can see the diff, judge the confidence, approve or roll it back, and trace what happened afterwards

• Keep lineage intact end to end, so any fact can be traced back to what produced it

• Keep the cost curve flat as volume grows. A method that works on a thousand records and falls over at a billion is not a method

• Decide where models earn their cost per record and where cheaper deterministic machinery does the job just as well

• Own evaluation for your layer. Golden sets and regression suites, LLM-as-judge where its failure modes are understood, and accuracy, cost and latency tracked per record

• Evaluate the whole trajectory on multi-step runs. A correct final answer reached by a broken path is a defect

• 5+ years of applied AI or ML engineering, with work that reached production and changed something

• Very large scale, not negotiable. You have run inference over billions of records and can talk through what actually broke: throughput ceilings, rate limits and back pressure, partial failure halfway through a…