Software Dev Engineer II, SIFT

About the role

Every day, the people closest to our customers and partners learn things that matter — in meetings, in deal rooms, in the field. For years most of that went nowhere useful: stuck in an inbox, a memory, a note no one opened again. We changed that. We built a product that captures those raw field inputs, automatically finds the patterns hiding across thousands of them, and uses AI to turn what used to be hours of manual report-writing into minutes — and it's now part of how a large, fast-growing community does its job every day.

That's the part that makes this interesting: it already works, at real scale, for a lot of people — and that raises the bar rather than lowering it. The hard problems now are the ones you only get to when something is genuinely in production and genuinely depended on. How do you keep quality high as usage climbs and the data gets messier? How do you make AI-generated output people trust instead of double-check? How do you extend a system people already rely on without breaking what they count on? This is applied AI in the critical path of real work — where "mostly right" isn't good enough and someone notices the moment it's off.

That's where you come in.

Here's the kind of thing you'd own and solve:

- Scale what's already working.

Take services that are live and heavily used and make them faster, sturdier, and ready for the next order of magnitude of users and data.

- Grow what the product can connect to.

Expand the set of tools and integrations the system exposes through the Model Context Protocol (MCP), so the product can plug into more data sources and let AI assistants do more on a user's behalf — safely and reliably.

- Raise the quality bar on AI output.

Improve how the product turns messy, free-form input into trustworthy, structured, searchable records — with smarter summarization, tagging, and quality gating so strong output ships and weak output doesn't.

- Extend the product thoughtfully.

Add new capabilities and data sources to a system people already depend on, without disrupting the workflows they rely on every day.

- Make the trends sharper.

Improve how the product spots patterns across a huge body of individual stories, so people see what's emerging without digging for it.

- Let people talk to their data.

Build and refine the natural-language experiences that let someone ask for what they need — and get an answer built from real data, and only the data they're allowed to see.

- Keep it healthy.

Own the reliability and operations of what you build, dig into production issues when they happen, and raise the operational bar for the whole team.

What sets this apart: you're joining something that already succeeded — and the interesting work is everything that comes next. You get the best of both worlds: the real users, real feedback, and real impact of a product in heavy use, plus a wide-open set of hard problems around scaling it, deepening the AI, and taking it places it hasn't gone yet. You'll make architectural calls that matter, see your impact quickly because the people using it will tell you exactly what's working, and be expected to lift the engineers around you — mentoring, reviewing designs, and being someone who makes the whole team sharper, not just your own code.