# The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra AI Engineer · 2026-07-28 Natalie Meurer argues that forward deployed engineering (FDE) has become a meaningless label because it has stretched from DevOps at Palantir in 2008 to data integration, ontology work in Slate and Foundry, solution architecture, and enablement—but its durable core is customer accountability and outcome-based pricing. Tracing FDE's history at Palantir, she shows how the role evolved from keeping the platform stable (2008) to data integration (2012), custom dashboarding in Slate (2016), and finally customer enablement in Foundry (2020). As coding agents make software cheap, she contends the lasting value lies in integrating data, understanding customers, and owning outcomes. Pricing tells the story: seat-based assumes a tool, while usage or outcome pricing puts the provider on the hook—exactly what FDEs have always done. She concludes agent engineering is FDE reborn, and that product, infra, and AI engineering are all trending toward the same customer-accountable model. ## Questions this episode answers ### What is the dirty secret of forward deployed engineering according to Natalie Meurer? Natalie Meurer argues that forward deployed engineering doesn't truly exist as a coherent role. At Palantir, the title expanded from DevOps and platform stability to data integration, dashboard building, and customer enablement, so it now describes many different jobs. The label means so many things that it has essentially lost any specific meaning. [2:43](https://aiengineer.podhood.com/170d0b43-371d-4029-aaf6-5e2219fbfd62?t=163000) ### How did the role of forward deployed engineer evolve at Palantir over the years? According to Meurer, Palantir's forward deployed engineers started in 2008 focusing on platform stability and DevOps. By 2012, they added data integration and ontology modeling. In 2016, they built custom dashboards using Slate. By 2020, they shifted to enabling customers to use the platform themselves, and now the role encompasses all these accumulated responsibilities. [3:15](https://aiengineer.podhood.com/170d0b43-371d-4029-aaf6-5e2219fbfd62?t=195000) ### Why does Natalie Meurer say that agent engineering is actually forward deployed engineering? Meurer explains that agent engineering shares the same level of customer accountability as forward deployed engineering. With coding agents making software cheap, the core value lies in integrating data, understanding the customer, and being responsible for outcomes. She originally saw agent engineering as a subset of AI engineering, but now believes it is essentially a flavor of forward deployed engineering, given that everything is converging toward outcome-driven customer work. [15:16](https://aiengineer.podhood.com/170d0b43-371d-4029-aaf6-5e2219fbfd62?t=916000) ## Key moments - **[0:00] Introduction** - **[1:54] Agent Engineering** - [2:43] The dirty secret of forward deployed engineering is that the term describes so many different jobs it essentially means nothing. - **[3:46] Palantir Origins** - **[4:51] Platform Stability** - [4:51] Palantir's 2016 forward deployed engineer onboarding project was deploying the software on an EC2 instance. - [6:03] Palantir's data integration software was useless without forward deployed engineers to integrate the customer's data. - **[6:37] Data & Ontology** - **[9:02] Not One Thing** - [10:11] A forward deployed engineer's 'vintage' — 2008 DevOps, 2012 data integration, 2016 Slate solutions, 2020 enablement — defines their skill set. - **[10:46] FDE Vintage** - [11:20] Hiring for forward deployed roles combines demands for a staff engineer with years of sales, solution architecture, and teaching experience. - [12:17] The durable part of forward deployed engineering is accountability to the customer; as code gets cheap, FDEs build end-to-end solutions. - **[12:28] Cheap Code** - [13:48] Software pricing will shift from seat-based and usage-based to outcome-based as agents increase autonomy and accountability for results. - **[13:55] Outcome Pricing** - **[15:26] FDE Reborn** - [15:33] All engineering roles — product, agent, AI, solutions, customer — are converging toward forward deployed engineering's customer-outcome model. ## Speakers - **Natalie Meurer** (guest) ## Topics AI Strategy, Agent Engineering ## Mentioned Airbus (company), Anthropic (company), Emergence Capital (company), OpenAI (company), Palantir (company), Sierra (company), AIP (product), Foundry (product), GCP (product), Skywise (product), Slate (product) ## Transcript ### Introduction **Natalie Meurer** [0:13] Thanks, guys, for coming. I knowright after lunch this is, like, prime time, like, sleepiness. I'm curious, just to get a sense for the audience, who here is a forward deployed engineer? OK, who here is hiring forward deployed engineers? OK, helpful. Anyone else in between? You can raise your hand, just like, if I didn't cover it. OK, I see you. Double, double hand raise. Well, I'm Natalie Meurer. I am the head of agent engineering at Sierra. I've been at the company about two years. I'll tell you a little bit about that. But honestly, I'm here to talk about forward deployed engineering. So we're actually going to spend a little bit less time talking about Sierra and a lot more time talking about the history of forward deployed engineering and the dirty secret, I like to call it, of the domain. And you all can tell me how controversial it is. So just a little bit about me, which will give you a sense for, sort of, how I'm coming at this domain, at the whole domain of forward deployed engineering. I actually started off as a policy nerd. So I was at Georgetown. I studied in the School of Foreign Service there. And so I was obsessed with, basically, tech policy. I learned to code and then swindled my way, or earned my way, depending upon your perspective, into a job at Palantir where I spent 2016 to 2021. And there, I worked on the privacy team. And I was an infrastructure engineer and also a forward deployed engineer with law enforcement and defense, primarily. So I've seen forward deployed engineering up close. And I'm going to talk a lot about the history of not just my time at Palantir, but actually the time that predated me, to talk about, basically, the origins of the entire discipline. And then I actually wanted to get a business degree, of all things. So I went to Stanford. And now I'm at Sierra from '24 to '26. And one of the things that I think is quite interesting, and when I started at Sierra, actually, a little-known fact, is that I joined the deployment team. ### Agent Engineering **Natalie Meurer** [1:58] And at the time, I hated the name. I thought, this is not what we're doing. And be it, it sort of is a nonsensical name, so to speak. And so I actually wrote an article in July of 2024, so about six months after I started, about two years ago now, on this concept of the agent engineer. And the idea was that agent engineering was actually a subdiscipline of AI engineering, which we're, of course, here to talk about. And the vision was that agent engineering had the same level of customer accountability as forward deployed engineering, but was actually a domain unto itself. And I think since then, you know, in the past two years, we've actually seen a lot of folks talking about agent engineering as a discipline. And now we're actually seeing harness engineering as, sort of, a new discipline, sort of as almost a subset of agent engineering. And so if you're confused, that's sort of what this talk is about. And so the dirty secret of forward deployed engineering, I won't make you wait until the end, is it doesn't exist. It is a term that is meant to describe so many things that it sort of means nothing at this point. But nonetheless, it's sort of the hottest job in AI. And so what I hope to convince you of over the next, let's say, 15 minutes is, A, that this is true, and, B, that this doesn't matter. And so I'm going to dive in. And we're going to start with tracing the history of forward deployed engineering from Palantir. So we're going to start in 2008. We're going to basically come back to the present. And then I'll share with you at least my perspective on what forward deployed engineering is today, why you should want to or not want to be a forward deployed engineer, and why we're actually all forward deployed engineers in the end. So first off, I'd actually like to start in 2016. And then we'll go a little back in time. And I actually pulled, basically, Palantir job postings in 2016. This is when I started. And what drew me into Palantir was, effectively, this concept of a forward deployed software engineer. ### Palantir Origins **Natalie Meurer** [3:50] And this was an emphasis on the forward deployed part. I mean, they're leading these days, you lead with, you know, you speak a certain language. You have these skill sets. They were leading with location. And there's a reason for that that we'll get to. But forward deployed literally meant sitting with customers, being on the ground. And in part, as we'll talk about, this was because so much of Palantir's ecosystem was on-prem deployments. And so when we think of forward deployed engineering, though, we kind of think of this. And so this is sort of the prototypical forward deployed engineerright out of college, parachuting out of a helicopter, and, of course, you know, fixing some sort of runtime issue in the air. But in practice, forward deployed engineering actually started as something more akin to DevOps. And so platform stability really constituted the bulk of the job early on. And so when you think about what forward deployed engineering actually was, don't worry about understanding this code on theright. But it just gives you a sense for what folks were actually doing. ### Platform Stability **Natalie Meurer** [4:51] In fact, my onboarding project at Palantir was actually to deploy the software on an EC2 instance. And so that was literally the job. And it was a large part of the job, and so much so that, basically, the job felt like this, which is basically an email from the customer telling you that someone mistakenly unplugged the instance again. Hey, could you look into it? By the way, it's 2:00 a.m. And you need to go in. And so in practice, a lot of the early forward deployed engineering was really focused on DevOps. And come 2012, the Palantir platform got a lot more stable. And Palantir built data integration software. So quick show of hands. Everyone here probably knows. But who here knows what data integration software is? Great. Anyone think data integration software is really useful without data? OK. OK, one allright. I'm curious to hear your take after. So I like to think of data integration software without integrated data as a movie theater that's playing nothing. It's like, why show up? You know? And so this is basically what Palantir was selling without forward deployed engineers. A, the movie theater wasn't standing in 2008. By 2012, you actually don't have data in it,right? You don't have movies playing. And so you sort of have this basic, you know, comic explaining this process, which is, hey, great. We have this data integration software. Isn't it useful? Right, but my data is in 20 places. And now you actually have a forward deployed engineer that needs to go in and integrate that data. And most of this was written in, you know, variants of Java back in the day, as was actually Palantir's client, fun fact, and Java Swing, if anyone here has had the privilege to use that. So basically, forward deployed engineers now had this dual role,right? DevOps plus data integration. And so they wanted to deeply understand the customer's environment to model that data appropriately. And what Palantir did then and now still does is call an ontology, which is sort of a famous Palantir term of art. ### Data & Ontology **Natalie Meurer** [6:56] But you can think of it as a taxonomy for that data. And then 2016 rolled around. And you have custom solutions. So folks are building, effectively, solutions to known problems. And usually, that took the form of a Slate dashboard. Has anyone here heard of Slate? This was sort of an internal OK, allright. So Slate was basically a drag-and-drop builder. It actually still exists as part of Palantir's platform. It allowed you to map, similar to VTool, actually, map components on the page to data sources. So now you have the data. And then the question became, OK, how do we make it useful? And the person, or the individual, or the group that was most well-situated to make that data useful was the individuals that most understood that data, which were the forward deployed engineers. And so this, basically, era of Slate actually gave way to an era of Foundry, which is Palantir's main platform today. And the era of Foundry was effectively all about making data useful. So Palantir had this phrase of going from data to decision-making, which was sort of the core problem. And one of the things they found, a spoiler alert for anyone building a dashboarding platform, is that a dashboard that doesn't write back to the data source isn't that useful, that these things would decay over time and actually not be as useful as they could be. Fast forward to 2020. You know, Palantir is on the verge of IPO. And they also need a solution that doesn't involve shipping people out to Finland, or Canberra, or choose the kind of location on the earlier slide of your preference. And they actually wanted to empower customers to do more of this work themselves. And so this is where Palantir really started to become a platform. And the job of forward deployed engineering still involved the first three. But it also involved enablement of those customers. So if you look at the work that Palantir has done with Airbus, for example, in Skywise, that was focused on enabling thousands of Airbus engineers on the Foundry platform to do work of the forward deployed engineer. And these days, they actually have a platform, since I left, called AIFDE that's focused on leveraging large language models to do similar work. So this is the timeline. And you'll see that it's not one thing. And so this is a picture of Alex Karp. And I think we might have lost the photo over here, but of Alex Karp basically running an AIP boot camp. ### Not One Thing **Natalie Meurer** [9:16] So this is basically where the forward deployed engineers, or the deployment strategists, would go in and actually teach customers to use the software. So what's interesting about this is these weren't discrete eras,right? The total FDE jobs to be done actually increased over time. Maybe they peaked at one point. But they actually grew over time. And so now we have a world, come 2020, 2024, where the role of FDE, sort of, doesn't mean anything quite yet. Or it means everything,right? It's actually the best training ground for generalists. And in fact, this is why I would say that Palantir has seen such a success of Palantir alums founding companies, is because it's actually training you on all of these different facets of the role. And so I have an idea that I like to think of that you all can ask your next candidate if you're interviewing for a forward deployed engineering role, which is, what vintage of FDE are you? Are you the 2008 vintage of platform stability with hints of panic, 2012, 2016, 2020, or even, you know, today? What is the forward deployed engineer of today? And so I like to call this the FDE vintage. And you might debate the years and debate the details. But just like fine wine, I think there's a particular vintage of an FDE. So welcome to 2026. Here we are. And FDE is absolutely everywhere. So I'm sure you all have heard Google recently announced, with GCP, an effort to hire customer engineers or FDEs. ### FDE Vintage **Natalie Meurer** [10:49] OpenAI created a new unit with a massive investment to aid the corporate AI push. You have folks like Aaron Levy basically posting on X, as well as many others, about just how important this work is today. But if you think about what we just saw, the work isn't one thing,right? So forward deployed engineering is actually many things all at once. And in fact, today, if you look at basically what it takes to be a forward deployed engineer, if you were to combine all the job postings into one, you'd probably see something like this. You know, I don't know how many of you have been a staff engineer for eight years with six years of direct sales experience and four years as a solution architect. Also, I don't know if you've taught in schools prior, because that would help as well. And this is actually what it feels like hiring for these deployed roles, as I've done for the past, you know, two years, two and a half years or so. But this is actually what's being asked of forward deployed engineers today. And so you think about the dirty secret that it doesn't exist. And actually, it either doesn't exist, or maybe it actually exists too much. Hard to say. And part of this, I think, and part of, sort of, I think the importance of the role is that across all of those different aspects of the role, the one continuity point is that you actually have every single forward deployed engineer accountable to the customer, whether that be for DevOps, for enablement, whether that be for custom solutioning, data integration. These are all forms of solutions or customer accountability. And when that happens, though, I ask the question, you know, what happens to engineering broadly, even outside of forward deployed engineering, when code becomes cheap to produce, when it becomes really quick to fire off a prompt to a background agent and get something pretty great on the other end? ### Cheap Code **Natalie Meurer** [12:38] And one way to think about this is forward deployed engineers being accountable to customers and trying to translate that signal into the product, either to make it more stable or to drive more data integration. And so what this allows you to do as a forward deployed engineer today is actually be way more impactful in product development. So forward deployed engineers can now not just talk to customers, not just prototype, but actually build end-to-end solutions with coding agents. But I think something more interesting is happening that we're seeing at Sierra, which is actually the lines are blurring. So product engineering is also becoming more client-facing. And so if you're a good product engineer, if you're a good forward deployed engineer, you should be thinking about the product and the customer both together. And so one of the reasons that folks are talking about forward deployed engineering as so essential to where we are at this point in time is that it's actually converging. Forward deployed engineering is, in some ways, actually getting larger than we ever thought it was before. So it's actually stacking more skills on top. And if you're a product engineer, even an infra engineer, you should also be thinking about DevOps,right? How do you actually deploy the software? And then separately, a different shift is changing when code becomes cheap. And this is actually from both Emergence Capital and then a blog post from someone on our team, our head of go-to-market ops, Elliot Greenwald. And the way that we sell software is changing. ### Outcome Pricing **Natalie Meurer** [14:04] And at Sierra, we have always been focused on this outcome-based pricing model. We think that you should pay a software platform for the value that that software delivers. And this is not always possible. So if you think about scenarios where, basically, you can only slightly attribute the outcome to the product, think of seat-based pricing. This is basically the way that software has been priced for an incredibly long time. And then you think about the agency and the autonomy to achieve the outcome. And agents basically move us up and to theright here. So usage-based, a good example of that might actually be what we pay to OpenAI, or Anthropic, or some of the foundation model providers, because it's based on usage. And it's hard to actually attribute the outcome. When you think about customer experience in AI, that's outcome-based. It's, are you actually making a sale? Are you solving a customer's inquiry? And we think, basically, that most of pricing in this market will move to outcome-based. So if you have both of these things, you have forward deployed engineers that can now contribute to the product. You have more outcome-based pricing. How do you actually guarantee the outcome? And that really is forward deployed engineering. And so if I jump here back to, sort of, where I started, this is how I envisioned agent engineering in 2024. And I don't know if I was wrong, or you guys can tell me if I was wrong. But it wasn't the whole story,right? That agent engineering actually was a subset heavily focused on outcomes. It is a flavor of forward deployed engineering. ### FDE Reborn **Natalie Meurer** [15:33] But these days, I'm actually of a different mind, which is that, really, in some ways, everything is forward deployed engineering, or at least everything is trending that way: product engineering, agent engineering, AI engineering, solutions engineering, and customer engineering. When you think about enablement, you think about DevOps. You think about solution building, data integration, building an agent, deploying it into production. These are things that are on behalf of customers at the end of the day. And we should be pricing outcomes associated with them. And forward deployed engineering, as a concept, even if it is one, sort of lacks a coherent definition, is something that actually allows us to enable those outcomes. So I'll leave you with maybe one last note, which is that forward deployed engineering is dead. And long live forward deployed engineering. So thanks so much. And by the way, we're hiring across forward deployed engineering roles at Sierra. So I told you it wouldn't be about Sierra. But if you want to talk about FDE, I'm here. Thanks, guys. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.