New Grads Make Great FDEs: A Conversation with Priya Khandelwal
Founder & CEO of Nixo (YC S25)
Priya Khandelwal was a machine learning FDE at Kumo.AI in 2023, before most people had heard the title. She went on to research at Stanford’s AI Lab and Scale AI before founding Nixo, the first ops platform for FDE teams, through Y Combinator. We talked about what she saw break as an FDE, why strong engineers still fail in the role, and why she thinks the real bubble isn’t FDE but the belief that generalist AI will work.
You were an ML FDE at Kumo back in 2023, before FDE became a popular title. What did you see during that time that made you think the way these teams operate is broken?
When I got the role, I wasn’t even sure what it was. The chief scientist at Kumo, who is the leading specialist on graph neural networks, told me I’d like it because it still involved the research-oriented components I needed, but it would be more customer-facing. So I gave it a try.
My job was to create graph neural networks that ingest sales data from our customers and then make predictions on things like churn and lifetime value. Very technical solution, very non-technical audience. I got to work with customers like DoorDash, Louis Vuitton, and Snowflake, actually going on site, understanding their sales team’s needs, then building models for them.
I totally got why the role is so useful. Without that FDE team, the product has no value for the customer. But the function was so nascent that we were figuring out everything on the fly.
One example that stuck with me: one of my colleagues was building a GCP connector, which I didn’t know about. My customer asked about building the same thing, and I started building it from scratch. Only after did we realise we’d both built the same thing. Through those hiccups, we realised there were so many ways we could be organising our customer accounts and the work items created for them better.
I honestly put it in the back of my mind for a long time. Then in June 2025, FDE hiring just absolutely took off. I started talking to companies and it surfaced that FDE teams are scaling fast, and there’s a good chance they’re going to run into the exact same issues I did back at Kumo. That has absolutely been the case.
You now talk to a lot of different FDE teams through Nixo. What do you still see as a common way that teams fail, despite having strong engineers?
Engineering is a big part of FDE, of course. If you have good engineers, you can implement fast, and that’s a big part of what the role is about: delivering the customer win quickly.
But the strongest FDE teams are able to minimise the amount of new work they have to do for each customer. That’s more of a technical project management skill, and it’s a very new one.
FDE involves insights from both pilot and pre-sales. There’s relationship building, understanding the customer’s unique needs. What separates it from a traditional engineering role is that relationship building and the context you’re carrying over. A lot of people compare it to sales engineering, but you actually have to deliver.
Most teams still treat FDE like just another engineering division where you just happen to know the customer a little better. It’s actually a lot more complex than that. Unless you take a very organised operational approach to managing your customer accounts, each customer becomes its own project instead of something that converges into your main product.
That’s the issue I still see a lot of FDE teams running into. They aren’t really running a SaaS business anymore. They’re running a services operation. The best FDE teams maintain that SaaS-like scale even though they’re handling individual accounts.
When Priya applied to Y Combinator with the idea for an FDE ops platform, I was curious how much explaining she had to do. And whether, from where she sits now, FDE is actually mainstream or whether we’re all just in a bubble.
When you pitched “ops platform for FDEs” at YC, how much did you have to explain what an FDE even is?
I’ve actually had an easier time talking about FDE to investors than to companies. Most investors tell me the number one issue that comes up in their board meetings is how to get the FDE process right. Their portfolio companies are encountering it constantly.
That was absolutely the case with YC as well. The partners were very aware of the FDE motion because a lot of the best YC companies have really thrown themselves into it. My general partner had seen HappyRobot go through these struggles and absolutely understood the market potential.
With companies, it’s a toss-up. Sometimes people aren’t aware they’re experiencing an issue. I encounter FDE teams where I look and think that’s a bit of a hot mess situation, but they’re not aware. They say things like “this is just startup life, things are messy, you have to be scrappy.”
Surprisingly, this isn’t really related to company stage. I’ve seen really mature seed-stage FDE teams that get the issue and are actively working to make their motion more scalable. And I’ve also seen Series C and D companies with massive FDE teams who are trying to solve the services problem by increasing headcount instead of improving operational efficiency.
Where do you see a bigger gap right now: figuring out how FDE teams should operate, or hiring the right people for them?
They’re equally challenging, and it’s a realisation I had not too long ago.
In the first few months of running Nixo, I would see two buckets of questions. The first is: how do I make my FDE team better? That starts with questions as simple as how should I organise my team, which gets into pod structures, making sure you have both specialists and generalists. Then it goes down to how do I templatise the code I’m creating, which is where infra tooling comes in.
The second bucket: do you know any good people I could hire? Because FDEs are like mini founders. They own customer relationships and they’re building the entire sales cycle and implementation. These are people you really need to trust.
That’s when I realised the two go hand in hand. People are trying to scale their FDE teams because FDE is absolutely business-critical for AI companies. They see that if they want to take on more customers, they need the bandwidth. So they hire. But headcount doesn’t solve all problems. In engineering, if a project is going to take a year, throwing five versus ten engineers on it doesn’t necessarily change that. Maybe nine months instead of a year, but it’s not meaningfully different.
Nixo actually started a Hiring Hub programme because we kept getting people on both sides, employers looking for the best FDEs and FDEs asking where they should work, so we started matching them. One of our customers is in the healthcare space doing implementations that require EHR integrations and selling to CMOs and CTOs. Being able to look at our network and say, here’s a former founder in health tech who has sold to CMOs, let me make that intro, that’s become a real part of what we do.
But even outside of that, I always remind companies: not everything can be solved by adding headcount. When you’re looking to increase what your FDE team can take on, the most important thing is to hire people who 10x the leverage of your team. That means people who are high energy, enthusiastic about customers, have built zero to one before, and can manage their own technical projects. They can scope out what needs to happen now versus later. That’s actually the hardest part of being an FDE.
You mentioned high energy and enthusiasm. Here’s a question: what’s your take on experience level? The median FDE has about a decade of work experience. Is that what teams should be looking for?
My hot take is that new grads actually make really good FDEs.
Despite the lack of working experience. Because what I’ve come to realise about FDE is it’s a scaled-down version of making a startup. A lot of founders believe you learn by doing. You can have some experience, but you learn the most by doing. The same goes for FDE.
If you have high-energy, hard-working, enthusiastic people who care about customers, put them in that situation. Maybe they’ll need a little bit of hand-holding, but they’ll get it. Some of the best FDE teams I admire have experimented with bringing new grads onto their team and have seen a lot of success.
I read the article where you analysed the median years of experience for FDEs. My takeaway was that it’s more effect than cause. People believe you need experienced folks on the ground, so they hire experienced folks. But I’ve noticed in the best FDE teams, the bar is really having high energy, enthusiasm, and technical skills. Experience can be nice to have depending on your industry, but you can get those qualities from new people too.
Sometimes the best founders are folks who are in college or right out of college. They have no experience, but they’re approaching problems from an entirely fresh perspective. They’re more optimistic than people who have been in the industry, so they’ll find a way to cut around corners and get the win fast.
For some industries, that’s not always the case. If you’re doing really technical implementations, like deploying ML solutions in production, it helps to have people who’ve done that outside of a research environment. But even then, someone with one or two years of experience can do fairly well. I’ve seen people who were solutions architects for years get outperformed by newer people who just have that hunger.
You mentioned being honest with yourself about whether FDE is a real long-term need or just a moment. What’s the conclusion you arrived at?
I was grappling with the bubble question a lot during Y Combinator. I graduated in 2025. Coming out of college, I had an offer to join a foundation lab as an ML researcher. I had to ask myself: do I really want to throw that away for this?
The conclusion I arrived at was this. Ten to fifteen years ago, when you looked at SaaS, a lot of problems could be solved by generalised, off-the-shelf solutions. That’s why the SaaS model was really thriving. You could have software that works 80% out of the box, and then maybe 10 or 20% you have a solutions architect come in, tie the bow, and make it land for the customer.
But we look at the AI era, and AI works really well in hyper-specialised situations. We don’t have a good generalist AI yet. And, to be frank, AI is still pretty bad. It doesn’t really work reliably. That’s why everyone is trying to duct-tape together these sort-of-working solutions: agents, workflows, trying to make it work.
We’re seeing the exact opposite of SaaS now. An out-of-the-box solution can only deliver 10 or 20% of the value, and you need a human being to really understand the customer’s situation and make it work.
I asked myself: is this a consequence of a new industry where everyone can easily get funding and hype, or is this a more structural problem? My background is in AI and ML research. My understanding of LLMs is that as long as we stay on the current transformer-based paradigm, we don’t have intelligence. We need a lot of hand-holding with LLMs. Until we hit a research breakthrough, this is going to be the mode of operation.
That’s why FDE has become so important to AI. AI needs a lot of hand-holding right now. If you’re not getting FDE right, your AI business could collapse.
I think the real bubble is people thinking they can get away with generalist AI solutions when the technology is not there yet. The companies that will emerge from this bubble are the ones that have understood the limitations of AI and put workarounds in place.
When I hit that realisation, I went all in on Nixo. This is going to be a critical business need.
Priya Khandelwal is Founder & CEO of Nixo (YC S25).