Get hired atRIDGE AI×The Anti Job Board

How to Actually Get Hired

Who reads applications, which channel gets a reply, and what they screen for.

<50 peopleSeattleAI

What gets their attention

Four people, closed beta with weekly customer cohorts, a pre-seed led by the most credentialed analytics investor roster imaginable, and a press release that specifically names "data scientists and engineers" as the expansion focus. The founding team is technically exceptional but small: Ellie Fields on product and CEO, Jeff Heer as chief scientist, Andy Caley as founding engineer (ex-Tableau), and Fritz Lekschas as founding research engineer (Harvard PhD, 20+ visualisation publications). The next hires are likely a second or third software engineer who can work on the browser-native rendering stack and AI data agent layer, a data scientist who can improve the recommendation systems for dashboard generation, and eventually a commercial hire as beta converts to paid accounts.

Why applying the normal way doesn't work

At <50 people, RIDGE AI has no recruiting team. Your application lands with a founder who is also running sales, product and payroll. The obstacle isn't a queue or an ATS, it's being seen at all. Cold outreach outperforms the form here, consistently.

Who to contact at RIDGE AI

Who decides:a founder or department head
Best channel:LinkedIn or direct email

Founding team

EF
Ellie Fields
Co-Founder and CEO

Ellie is one of the most credentialed product leaders in Seattle's technology ecosystem, and her specific combination of experience is what makes her the right founder for Ridge at this moment. She holds a BS in Engineering and a BA in Policy from Rice University, followed by an MBA from Stanford's Graduate School of Business. Her career began with a short stint at Microsoft before she joined Tableau Software, where she stayed for twelve years and rose to Senior Vice President of Product Development. Those twelve years span the entire formative arc of Tableau's growth: she was part of the core team that conceived, built, and launched both Tableau Public (the free community product that drove viral adoption) and Tableau Online (the cloud-hosted SaaS offering that drove enterprise subscription revenue). Her nickname within Tableau is "the Godmother of Tableau Public," which captures something important about her product instincts: she built the community product that made Tableau the default data visualisation vocabulary for an entire generation of analysts. She led product strategy and engineering across mobile apps, collaboration, search, and content experience during the period when Tableau went from a fast-growing startup to a Salesforce acquisition at $15.7 billion. She then became Chief Product and Engineering Officer at Salesloft, leading engineering for a sales workflow platform where she directly experienced the problem Ridge is solving: "I routinely experienced the problem where our team was creating real value for customers and had no good way to prove it. We would spend months building dashboards instead of focusing on what made us different." That quote, from the Ridge press release, is not a founder crafting a narrative. It is a CPO who spent months of her own team's engineering capacity building analytics dashboards that should have taken hours. Ridge is the product she wished she had at Salesloft. She is active on LinkedIn and has written thoughtfully about the transition from large company executive to early-stage founder.

JH
Jeff Heer
Co-Founder and Chief Scientist

Jeff Heer is, without overstating it, one of the most consequential computer scientists in the history of data visualisation. His academic pedigree is from UC Berkeley, where he completed his BS, MS, and PhD in Computer Science, and his career has spanned both pure research and applied commercial work in a way that is unusually rare. As a graduate student at Berkeley, he developed Prefuse and Flare, two of the first widely-used visualisation frameworks. He then joined Stanford's CS faculty as an assistant professor from 2009 to 2013, where he worked with Mike Bostock on the Protovis and D3.js visualisation systems. D3.js is now the foundation of almost every interactive data visualisation on the web. He moved to the University of Washington, where he co-directs the Interactive Data Lab and has continued a research programme that produced Vega, Vega-Lite, and most recently Mosaic, the open-source framework that Ridge's entire platform is built on. Mosaic is architecturally significant: it is an academic research output that enables highly interactive, browser-native analytics using DuckDB and WebAssembly, and it is now a production commercial product at Ridge. His research has received essentially every major award in the HCI and visualisation field: MIT Technology Review's TR35, an Alfred P. Sloan Fellowship, the ACM Grace Murray Hopper Award, the IEEE Visualization Technical Achievement Award, and induction into both the IEEE Visualization and ACM CHI academies. He was named an ACM Fellow in the 2024 class "for contributions to information visualization, human-centered data science, and interactive machine learning." His citation count on Google Scholar is 39,699, he is cited by researchers at Google, Microsoft, Adobe, and essentially every major technology company that builds data products. He co-founded Trifacta in 2012 with Joe Hellerstein and Sean Kandel, which built interactive data transformation tools and was acquired by Alteryx in 2022. His student list on his faculty page is itself a who's who of the data industry: Mike Bostock (D3.js, then New York Times, then Observable), Ravi Parikh (Heap, then Airplane, then Airtable), Dominik Moritz (now CMU faculty), Arvind Satyanarayan (now MIT faculty). When Jeff Heer founds a company, the research that powers it has been proven over decades in both academic and commercial settings. Ridge is not a bet on whether the technology works. The technology already works. The bet is whether it can be packaged and distributed at commercial scale.

What to show them

Ridge's platform is architecturally unusual: it runs analytical queries inside the browser using DuckDB and WebAssembly rather than on a server. Building features on top of this architecture, extending the Mosaic framework, and integrating the AI data agent layer requires someone comfortable working at the intersection of JavaScript/TypeScript frontend, embedded database systems, and LLM integration. The founding engineering team comes from Tableau and AppOmni, which means the culture values rigorous product engineering over hacking. Core skills: TypeScript/JavaScript, WebAssembly or DuckDB experience (strong plus), data visualisation libraries (D3.js, Vega, or Mosaic directly), LLM API integration, frontend performance engineering, embedded systems or in-browser database experience. Proof of work: Fork the Mosaic open-source framework (github.com/uwdata/mosaic), build a small interactive dashboard on top of it that renders a meaningful dataset, and add a minimal natural language query layer that translates a plain English question into a SQL query against the DuckDB instance. Share the repo. It is the exact technical stack Ridge is building on, and shipping something with it shows you have already done the orientation that takes most candidates a week.

A cold email that works at RIDGE AI

Subject: Software Engineer (Browser-Native Analytics / AI Stack), [your one-line proof]
Hi Ellie, I forked the Mosaic framework and built a small dashboard on top of it with a minimal NL-to-SQL layer, as a way to understand the browser-native architecture problem before reaching out. There were a few DuckDB edge cases I ran into around query cancellation that I thought were interesting. Happy to share the repo and the write-up.

What RIDGE AI screens for

Screening signal: Four people, closed beta with weekly customer cohorts, a pre-seed led by the most credentialed analytics investor roster imaginable, and a press release that specifically names "data scientists and engineers" as the expansion focus. The founding team is technically exceptional but small: Ellie Fields on product and CEO, Jeff Heer as chief scientist, Andy Caley as founding engineer (ex-Tableau), and Fritz Lekschas as founding research engineer (Harvard PhD, 20+ visualisation publications). The next hires are likely a second or third software engineer who can work on the browser-native rendering stack and AI data agent layer, a data scientist who can improve the recommendation systems for dashboard generation, and eventually a commercial hire as beta converts to paid accounts.

A tailored CV beats a generic one. Use RIDGE AI's job description language to clear filters.

Mistakes that kill applications

A recycled CV gets rejected fast at RIDGE AI (<50 people). They notice.

Skip 'I'm looking for...' — start with The embedded analytics market has a technical debt problem so large that the entire category is essentially being rebuilt from scratch and your specific angle on solving it.

The wait-and-hope strategy fails. Follow up on day five — response rates roughly double.

Frequently asked questions

How do I apply to RIDGE AI?

Through the roles on our RIDGE AI jobs page, or directly to a founder or department head if you can reach them. At <50 people, direct outreach outperforms the form.

Does RIDGE AI respond to cold emails?

Not enough data yet on RIDGE AI's cold email response rates.

Who is the hiring manager at RIDGE AI?

At this size, hiring is usually run by a founder or department head.

How competitive is it to get hired at RIDGE AI?

Typical applicant count for AI roles (<50 people): 50-100 in two weeks. Apply fast.

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