Senior AI Engineer

About the role

ABOUT METAFORMS

Market research runs on 30-year-old survey platforms and armies of specialists hand-coding questionnaires in proprietary languages. Metaforms is the agent layer that does that work. Every survey is a program — full of skip logic, piping, quotas, and loops — and a single wrong number in a client report is unrecoverable. Our AI agents write production survey code, QA live deployments, process and clean large structured datasets, configure analysis, and generate client-ready reports, so agencies like Dynata, Savanta, and Borderless Access ship more projects with far less friction.

- 1,000+ surveys processed monthly

- Serving Fortune 500 companies across the globe

- Rapid month-over-month growth

We’re Series A funded and scaling fast, aggressively growing our AI engineering team to build the next generation of production-grade AI agent systems.

THE ROLE

We’re hiring a Senior AI Engineer to own the design, development, and continuous improvement of the AI agent systems that power modern research operations.

This is a high-ownership, high-impact role at the intersection of applied AI and systems engineering. You’ll work on genuinely hard problems: agent reliability at scale, long-context handling, cascading error mitigation, and evaluation infrastructure — like codegen agents that write in proprietary DSLs, computer-use agents that QA live deployments, data agents that clean tabular exports and configure multi-step analysis, and evals for outputs where “correct” is genuinely ambiguous. And you’ll do it on a team that ships fast and treats quality as non-negotiable.

WHAT YOU’LL OWN

AGENT HARNESS AND ARCHITECTURE

- Own the agent harness our production agents run on — the loop where agents plan, use tools, check their work, and recover from failures

- Lead research and implementation for long-context handling and cascading-error challenges in multi-step agent pipelines

- Drive context engineering strategy and experimentation frameworks across the team

EVALUATION AND PRODUCTION MONITORING

- Define structured rubrics for evaluating AI outputs on nuanced, ambiguous research tasks

- Build continuous monitoring, tracing, and failure-mode analysis for agents in production — including the loop that turns production failures into test cases

- Create tooling that lets domain experts refine and evolve the skill files, eval sets, and knowledge bases our agents consume

RELIABILITY FOR HIGH-STAKES OUTPUTS

- Build eval suites — regression sets, golden datasets, LLM-as-judge pipelines — that catch regressions before deploy

- Develop evaluation datasets for DSLs, structured data transforms, and computed outputs to systematically find and close model weaknesses

- Design human-in-the-loop and review workflows for outputs where a single wrong number in a client report is unrecoverable