About Snowplow
Snowplow is the real-time customer context layer that collects, validates, enriches, and delivers behavioral data for advanced analytics, ML, and AI agent decisioning. Snowplow's event tracking is leveraged across 2M+ websites and applications globally, processing over one trillion events per month.
More than 250 companies, including Experian, AutoTrader, Strava, Condé Nast, and HelloFresh, rely on Snowplow to build a well-governed, first-party data foundation that powers their customer-facing AI agents, in-session personalization and recommendations, and real-time analytics.
This Opportunity
Snowplow is rebuilding how the company runs its own work. AI-native transformation is the operating priority for the business, and the Applied AI team owns it: the platform every team's AI work runs on, the pattern for how a function's critical workflows move onto agents, and the measurement of whether it worked. Individual AI adoption is already high across the company. The next stage is redesigning functions so that agents run the recurring work and people set the goals and judge the output.
You are the first Forward Deployed Engineer on the team, reporting to the Senior Director of Applied AI. The team is also hiring a principal engineer to own the platform and an Applied AI engineer to embed with our Growth team. The Senior Director you report to still ships code. You are the one who moves between functions. You embed with one function at a time alongside its leader and the people who do the work by hand today, map how the work actually runs, build the agentic workflow that takes it on, tune it with the experts until they trust it, and hand it back to the function to own. Then you move to the next one.
You sit with our major business functions, one at a time. The Senior Director of Applied AI and the function's leader choose each engagement, and your workflow maps are the evidence they choose from. Priorities will shift as the transformation moves, and the role is designed for that.
This is the forward deployed model applied inside our own company. You write the code, and you also do the discovery a consultant would. The function's leader owns the decision to change how their team works. You own the engineering and the pattern. You'll spend as much time listening to someone walk through a task they do by hand every week as you will writing the agents that run it, and the deliverable is a function that works differently, understands why, and can run what you built.
What you will own
- The workflow map. You sit with the people who do the work and leave with how it actually runs today: where work enters, what moves between steps, who says yes, and what "good" means. It is the first thing you do in every engagement.
- The case for whether to build. Before anything gets built, you bring the evidence from the map and make the case for whether the workflow is worth moving onto agents, including when the answer is that it does not need AI. The decision sits with the function's leader and the Senior Director; the evidence is yours.
- The build. You design and build agentic workflows on Snowplow's shared factory framework, which Applied AI runs: declared queues, holdback gates (the agent's write does not land until the ledger carries the approval), focused agents, and a central ledger that records every agent run. Every factory you build is compatible with every other one, in any function.
- Approval gates. You decide where the agent acts on its own, where it proposes and a person clears the holdback, and what gets recorded so anyone can see what the agent did and why. The people in the function keep the judgment. The agents take the rest.
- Agent readiness. You make the function's knowledge, systems of record, and quality bar explicit enough for agents to work from. Often this is the real work, and the agents are the easy part.
- The last twenty percent. Most of the work comes after the prototype: reliability, edge cases, evaluation, and a team choosing it over the way they worked before. You tune the output alongside the domain experts and encode their judgment into checks that hold after they step back.
- The handoff. You train the function to run and evolve what you built, then leave. Where an Applied AI engineer is embedded in the function, you hand it to them. Success is a workflow still running months after you have moved on, owned by people who are not you. After the handoff you stay available: when the function sees a problem or a way to improve the workflow, you coordinate it with the embedded and platform engineers rather than taking it back, and you help build the platform those workflows run on.
- The pattern itself. You own how a workflow moves from map to agents to handoff, written down so the next engagement starts from it. What you build also goes back into the shared framework as reference implementations. The second factory in any function should be cheaper than the first.
We would love to hear from you if you have
- Shipped real software in someone else's stack. You are comfortable being dropped into an unfamiliar codebase, dataset, or business system and productive in it fast. You are fluent in at least one of Python, TypeScript, or Go, and you pick up the others as the work needs them.
- Built with LLMs in production. You have designed agents or agentic workflows that run real work, with prompts, tools, evaluation, and the failure modes that come with them. You know the difference between a demo and something a team relies on every day, and you have done the unglamorous work of closing that gap.
- An AI-native way of working you can show us. A workflow, automation, or agent you built and still use. Expect to demonstrate it during the interview process.
- Sat with non-engineers and understood their work. You can spend a day with a sales, marketing, or customer team and come back with the actual process, including the parts nobody wrote down. Active listening matters here as much as the stack.
- Self-direction and comfort with ambiguity. Engagements are time-boxed, functions differ, and the next one is decided by the business. You plan your own work inside an engagement and stay steady when the business changes what comes next.
- The instinct to hand off. You would rather install a capability that keeps producing without you than become the person a team depends on.
- Low ego. You retire your own work when a better approach shows up, including one a teammate built. Models and tools change fast, and last quarter's build may need replacing.
- Clear communication with senior stakeholders. You can explain to a function leader what changes for their team, in their language, and hold your ground on the pattern when it matters.
- A track record you can talk through. You have worked in a forward deployed, solutions engineering, internal tools, or consulting engineering role, or you have transformed a process end to end inside a company you worked for.
How we hire for this role
The interview process includes a working session where you walk us through how you work with AI on a real task, using your own setup. We work this way ourselves, and you are welcome to use AI when applying.
What you get in return for being awesome
- Meaningful equity stake
- Competitive Base Salary
- Flexible working
- Generous PTO
- Medical, Dental & Vision Insurance
- 401k
- MacBook and home office equipment allowance
- Enhanced Family leave