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.

You're a strong fit if you:

• Have 3–6 years of experience in ML/AI engineering, with recent hands-on work building and shipping agentic systems (not just RAG chatbots) - ideally something a real user depended on.
• Have built or deeply worked with MCP (Model Context Protocol), or a comparable tool-calling / function-calling framework, and understand the practical failure modes of agent-tool interaction.
• Are strong in Python, comfortable with LLM orchestration (whether via raw API tool-use, LangGraph, Claude Agent SDK, or your own harness), and know when NOT to reach for a framework.
• Have production experience with evals - you can articulate how you'd measure whether an agent got better or worse after a prompt or tool change, and you've actually built that measurement, not just read about it.
• Are comfortable owning ambiguity: given a business use case (e.g. help a marketer trust a budget reallocation), you can scope it, prototype it, and ship a v1 without needing a fully-specified spec.
• Have cloud deployment experience.

Nice to have:

• Familiarity with MCP Apps / interactive UI resources, or building agent-facing frontends (we use TanStack Query/Router, Zustand).
• Experience with vector databases and RAG, even if this role is more agentic-orchestration than retrieval-heavy.
• A track record of 0 to 1 builds; you've taken something from nobody's built this yet to customers use this every day.

Our stack:

GCP, Java and Python, BigQuery, Spanner, MCP (remote server + emerging MCP App/UI resource work), A2-UI, AG-UI, Claude/Anthropic APIs, ADK 2.0, Langgraph, Agno, VertexAI, and a Vite-based frontend (TanStack Query/Router, Zustand).

What's in it for you:

• Ground-floor ownership of the agentic layer at one of the fastest-growing MarTech companies right now, your decisions on harness design and tool architecture ship to production customers within weeks, not quarters.
• Small, non-bureaucratic team with real empowerment; you'll work directly with the CTO and founding engineers, not through three layers of process.
• A genuinely hard, well-scoped problem: agentic reasoning over causal marketing models, where correctness actually matters to the customer's budget decisions.
• Competitive compensation and benefits, and a highly profitable, growing organization with real room to accelerate your career.
• A team that bonds over tea, movies, and Friday hangouts and takes work-life balance seriously.

Interested We'd rather see something you've built (an agent, an eval harness, an MCP tool, anything) than a polished resume - bring it along.