Applied AI Engineer

Who we are:

Lifesight is a Unified Marketing Measurement platform that helps marketers make better decisions. We're a team of 130 serving 300+ customers across five offices: the US, Singapore, India, Australia, and the UK.

We're building the world's best marketing intelligence and decisioning platform, and we've bet heavily on agentic AI to get there. MIA (our Marketing Intelligence Agent) is a production MMM measurement platform shipped as an agentic interface over the platform as well as remote MCP server and it's already how our customers query causal models, run budget optimizations, and diagnose saturation and halo effects in natural language. This isn't an R&D side project; it's core product, and it's growing fast.

Position overview:

We're looking for an Applied AI Engineer to own the execution of our agentic AI roadmap end-to-end, from agent harness and tool design through to evals, reliability, and the interactive experiences our agent surfaces to users. You'll work directly with the CTO and founding team on a system that already runs in production, not a greenfield prototype and not a research exercise.

This is a hands-on, majority-execution role: you'll be writing the orchestration code, designing the MCP tools and extensions, building the evals that catch regressions before customers do, and shipping the UI surfaces (charts, curves, optimizers) that make the agent's output legible.

What you'll do:

• Design and build agentic workflows on top of MIA's MCP server - tool definitions, complex reasoning and multi-step planning, context/session management, and error recovery for a system real customers query daily.
• Own the harness quality bar: work with frontier models (Gemini, Claude and others) to close the gap between "technically works" and "production-grade agent behavior". Prompt/Skills/system design, reflection steps, structured tool responses, and graceful degradation when tools fail or return partial data.
• Build and maintain evaluation pipelines for agent quality. Accuracy, tool-call correctness, hallucination/fabrication detection, and regression testing across our causal models and workspaces.
• Extend the MCP surface: design new tools, metadata extensions, and (where relevant) interactive MCP App / UI resources; so agent output isn't just text but something a user can act on.
• Work with structured marketing data (BigQuery/Spanner-backed outputs and translate it into agent-consumable context and user facing narratives based on the user's persona.
• Debug production agent issues systematically: stuck sessions, workspace binding failures, inconsistent tool outputs, model-vs-model disagreement and turn recurring failure patterns into fixes, not one-off patches.
• Stay current on the agentic AI and MCP ecosystem and bring back what's actually usable, new protocol capabilities, orchestration patterns, eval techniques; rather than chasing every new framework.