GTM Engineer (Applied AI)

GTM Engineer (Applied AI)

Location: Dublin or London

About Fin

Fin, now part of Salesforce, is on a mission to help businesses provide perfect customer experiences.

Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey, from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk, giving modern support teams one single system.

Together with Salesforce, the #1 AI CRM, where humans with agents drive customer success, we're building the future of customer experience. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword, it's a way of life. The world of work as we know it is changing, and we're looking for Trailblazers who are passionate about bettering business and the world through AI.

Ready to level up your career at the company leading workforce transformation in the agentic era? You're in the right place. Agentforce is the future of AI, and you are the future of Salesforce.

The team

GTM Engineering builds the tools that help our Sales and Customer Success teams win more deals, grow accounts and keep customers. The aim is tools that make a real difference to pipeline, win rate and retention, and give reps more time with customers.

To do that, we join data most GTM tools never see: how customers use the product, what was said on every sales call, how Fin performs for each customer, and everything in the CRM. On top of that we build agents and models that tell reps which accounts need attention, prepare them for calls, flag buying and churn signals, explain why deals are won and lost, and draft research and outreach.

The role

We're looking for a GTM Engineer to own a business problem from start to finish. You'll start with a commercial question or problem, you'll work out with reps and leaders whether it's worth solving and what success looks like. Then you'll build the data, the agent and the product, and get it into the daily routine of the people it's for.

Once it's live, you'll own making it better. You'll understand how it behaves in production, find where it fails, improve how it decides or acts, and work out whether it's making a difference. That includes deciding when something isn't working and should stop.

What you'll do:

• Build and ship AI agents and LLM-powered tools for Sales and Success, such as account research, call prep, post-call follow-up, renewal risk and pipeline review.
• Own the full stack: Python backends and APIs, data models in Snowflake, feedback pipelines, and React frontends where a tool needs one.
• Instrument what you build. Trace agent actions, tool calls, failures and the edits reps make to agent output, so you can see where quality breaks down.
• Set the quality bar with the people who use the tools. Build evaluation sets, grade output with human and model graders, and run regression tests and production monitoring.
• Diagnose weak results across context, data, tools, prompts and workflow design. Test the fix on replays of past traffic before it ships.
• Decide what an agent can do on its own and where a rep reviews its work first, and build the guardrails and review steps to match.
• Measure adoption and business impact on conversion, win rate, cycle time and retention. Use experiments and staged rollouts where you can.
• Get tools into teams' hands. Write the playbook, run the enablement session, and work with sales leaders so the tool becomes part of how their team works.

What you bring

• 4+ years building production software, applied AI systems or data products.
• You've built LLM-powered applications or agents that real users rely on.
• You've improved an AI system after launch using traces, user feedback, evals or experiments.
• Hands-on evaluation experience, such as building regression tests and grading output with humans or models.
• Strong Python and SQL. You're comfortable designing APIs and data models.
• You can measure impact with rigour and tell whether a metric moved because of your change.
• Product judgment. You can sit with a sales rep, work out what they need, and decide what to build first.
• You use AI coding tools heavily and know how to get reliable work out of them.

Nice to have

• Experience with GTM systems such as Salesforce, Gong or Outreach.
• dbt, Airflow or other data orchestration tools.
• Agent evaluation or observability tools.
• Time spent in, or building for, a sales, CS or revenue operations team.