Senior AI Engineer

Job description

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 WHAT WE’RE LOOKING FOR MUST-HAVE - Built and operated agentic systems in production — multi-step pipelines, tool use, codegen, computer-use, or data and reporting agents — not just prototypes - 4+ years of engineering experience, with at least 1 year focused on LLM/agent systems in production - Deep hands-on experience with frontier model APIs (Anthropic, OpenAI, Gemini), evaluation frameworks, and AI system optimization - Strong Python skills; Go or TypeScript a plus - Solid grasp of context engineering and evaluation methodology - Strong instincts for debugging complex, non-deterministic system failures - High ownership: you drive problems to resolution independently and pull others in when it matters NICE TO HAVE - Experience with LLM observability and eval tooling (Braintrust, Langfuse, LangSmith, Weave, promptfoo, or in-house equivalents) - Background in semantic parsing, DSLs, or structured-output generation - Prior work on computer-use or browser agents - Experience with human-in-the-loop agent workflows where proposals are reviewed before apply, or agents over large structured datasets WHY METAFORMS - Work at the frontier of production AI: systems handling 1,000+ research projects a month, with the reliability bar that implies - A small, senior team where your decisions carry real architectural weight - Zero-bureaucracy culture: high autonomy, fast feedback loops, direct access to leadership - Well-funded and financially stable, with a clear roadmap and the runway to execute on it BENEFITS - Full family health insurance - $1,000 USD annual learning and development budget - Dedicated mentor and coaching support - Free snacks and dinner at the office