Applied AI Engineer, GTM Growth Engineering

Responsibilities

• Own the production improvement loop across agent behavior, customer and operator feedback, evaluation, experimentation, and verified business outcomes.
• Instrument agent workflows so model interactions, tool use, decisions, failures, human edits, and downstream outcomes can be understood in context.
• Define meaningful quality standards, representative evaluation datasets, regression coverage, and production monitoring for real GTM workflows.
• Investigate why agents underperform across context, knowledge, instructions, tools, routing, guardrails, or workflow design.
• Design and ship targeted behavior improvements, including changes to prompting, context construction, decision logic, tool use, and human-review paths.
• Build backend services, APIs, data models, and feedback pipelines that make agent behavior observable, steerable, and reproducible.
• Run controlled experiments, production replays, or staged rollouts to measure whether changes improve quality and downstream business results.
• Partner with Product, Data Science, Sales, and B2B Marketing to prioritize high-value problems and define customer and business success.
• Ship with appropriate safeguards for privacy, security, reliability, human oversight, and safe operational rollout.

Requirements

• 4+ years of software, backend, applied AI, or product-engineering experience building reliable production systems.
• Experience building AI agents, LLM-powered applications, or other model-driven workflows that operated on real production traffic.
• Experience diagnosing and improving agent behavior using production traces, user feedback, evaluation, experimentation, or careful systems design.
• Practical experience with evaluation design, regression testing, human or model grading, online quality signals, or controlled experiments.
• Strong backend engineering skills across Python, APIs, data pipelines, stateful workflows, and production services.
• Strong product judgment and the ability to connect technical changes to customer experience, conversion, qualified pipeline, or operational efficiency.
• Comfort working across model behavior, context, knowledge, tools, workflow state, and human-in-the-loop decisions.
• The ability to work closely with technical and non-technical partners across Engineering, Product, Data Science, Sales, and B2B Marketing.
• A pragmatic mindset: you can scope ambiguous problems, ship useful improvements, and build toward a durable system.

Nice-to-Haves

• Experience building agent evaluation, observability, experimentation, or AI infrastructure products.
• Experience with production replay, LLM grading, human-labeled datasets, shadow evaluation, or staged rollout.
• Experience improving model or agent behavior through context design, prompting, tools, decision logic, or feedback loops.
• Experience with sales, B2B marketing, revenue, CRM, campaign, or other GTM-facing systems.
• Experience measuring customer engagement, qualified pipeline, conversion, or operational efficiency.