Forward deployed engineers & product: why they exist and how to prepare for their interviews
Updated: 24 hours ago
What these roles are, why AI companies are hiring for them, what makes them hard, and how to prepare for each part of the interview.
More of my coaching clients are asking me about the hottest trend: forward deployed PM and forward deployed engineers roles. This post covers why these roles exist, how they differ from the roles around them, what makes them hard, and how to prepare for each part of the interview.
What I learned in my own forward deployed role
I took on a forward deployed role myself before I fully understood what it meant.
A few months into a pilot automating parts of a client's sales execution, a senior executive wanted AI to drive every decision right away. The sales teams weren't ready for that, and I had already agreed with their leadership on a gradual ramp. I had one day to align everyone.
I started with a risk assessment. Were the teams ready to work this way, and was the system ready to carry that much of the load? Did a faster ramp still serve the commercial strategy we had agreed on? Then I sat down with the executive to understand what was driving the concern. We realized we didn't share a definition of what "AI control" meant, or how it would change the day-to-day work of salespeople and operations. Once we had that definition, I brought the executive and sales leadership into the same conversation to compare perspectives, walk through the risks and agree on next steps.
We kept the original plan. AI-driven actions grew steadily, and both revenue and sales productivity improved.
What I learned is that adopting AI is risky, and it's also scary, even for the teams that asked for it and are excited about it. People need to feel they're in the driver's seat, and that starts with clear, shared definitions of what the AI decides and what they decide. And more importantly how to course correct when something goes wrong
I also learned that acting in the customer's best interest comes first, even when a senior stakeholder is pushing for something else. Since then, helping clients land and onboard to these roles, I've seen the same pattern again and again.
Why are companies hiring for forward deployed roles?
Getting access and value from an AI product requires hands-on integration. If that isn't proven during the pilot, the customer won't sign and the revenue never comes. Search interest reflects how quickly this has become a priority.
Many enterprises hesitate to give AI vendors access to their data, out of fear of security breaches or autonomous agents gone rogue. Others don't have the talent to implement the technology correctly, or to maintain it once the vendor leaves.
These companies also have their own priorities and operating models. Changing habits to adopt a nascent technology is hard, and it competes with everything else teams are already responsible for.
Users who don't know what to do with a new AI tool often abandon it during the trial. That drop-off is expensive for a company that needs pilots to convert.
The person using the product is also often different from the person paying for it. I recently worked through an example with a client who built a product for field service companies. Technicians used it every day to diagnose equipment, file reports and get paid. The business owners who signed the contracts cared about utilization and throughput, and they couldn't see how the product affected either. Trials stalled because the economic buyer didn't see value, even though the end users were happy.
Forward deployed teams exist to close these gaps. They focus on a few accounts, find what's blocking adoption, and work within their own companies and their client companies to unblock it. That can mean identifying the data access required, ensuring ETL (extract, transform, load) processes are correct, and understanding customer context, use cases, security implications and how to apply the technology correctly. They also seek additional use cases and opportunities their companies should focus on for wider industry adoption.
How the roles differ
Forward deployed engineers own technical implementation. They integrate the product into the customer's environment, build data pipelines and custom workflows, and solve technical blockers on site.
Forward deployed Product Managers usually work alongside a small team of those engineers. They own discovery with the customer, decide what gets built, and translate what they learn in the field into requests the central product team can act on.
Growth PMs are a useful comparison. They work across many customers at once, think about the funnel and partner closely with go-to-market. A forward deployed PM goes deep on a few accounts and has to decide which of their needs should shape the core product.
All of these roles feed the same funnel with different skill sets.
Who the role suits
Forward deployed roles suit leaders in tech (engineers, product managers, program managers, engineering managers, solution architects and others) who love customer work and want to stay close to the product.
The role requires a confident service mentality. You need to understand the technology and the use cases well enough to earn the customer's trust, and you need real empathy for the people whose work is changing. Being of service also means knowing how to set boundaries, negotiate scope and push back when a request doesn't serve the customer's goals.
Much of the work is unglamorous. For every exciting AI capability you launch, you'll fix many paper cuts along the way (a broken data feed, a permissions issue, a workflow that doesn't match how the team actually works). That work is what gets the product across the finish line.
You'll also need to influence teams who may feel threatened by you or jealous of the new tools you get to work with. "Why do we even need this?" and "Can't we build it ourselves?" are some of the most common questions you'll hear from customers, and having clear, patient answers to both is part of the job.
In return, the role builds skills that will matter for years: scoping AI systems to fit enterprise constraints, deciding how much autonomy an agent should have, and bringing skeptical stakeholders along.
What makes the role hard
Competing priorities
Your accounts want things now. The central engineering team has its own roadmap. Every request from the field becomes a trade-off against something else the team planned to build.
That means you will sometimes go back to customers you've built close relationships with and tell them their request is on hold. In the field service example, shifting focus to business owners meant pausing technician features that pilot customers had been asking for. The PM had to explain that decision while keeping their trust.
The faster horse trap
When you're embedded with a customer, it's easy to solve only the problems they already know they have. Customer journey mapping and jobs to be done are useful starting points. Their limitation is that they surface known pain. The strongest forward deployed teams also look for the opportunity the customer can't articulate, and for patterns across accounts that point to where the product should go next.
The people inside the customer
Walking into an enterprise as the AI team triggers strong reactions from the people you work with every day.
Some stakeholders feel threatened. If this works, will AI replace my job?
Others feel jealous. Why does an outside team get to work on the interesting AI project while I keep the existing systems running? I want to build those skills too.
Even stakeholders who champion the project can feel uneasy once AI starts making decisions that used to be theirs. That's what I saw in my own pilot.
These reactions rarely show up as open resistance. They show up as delayed data access, slow approvals, meetings that keep getting rescheduled and lukewarm adoption after launch.
What helps is putting people in the driver's seat. Define clearly what the AI decides and what they decide. Frame the work around making their team more effective. Give internal people visible ownership and credit, and bring the curious ones into the build so they learn alongside you. The forward deployed team that wins these stakeholders over often becomes the reason the account expands.
Bringing engineering along
A request from the field can look like a completely different product to the engineers back home.
In the field service example, the PM brought a new design for the business owner experience to the engineering team. Engineers had spent months building for technicians. The new design was a standalone page with no clear connection to what already existed, so they worried their work was about to be thrown away. They also estimated it as a much bigger build than planned.
The concern was legitimate. The PM could have pushed back or escalated, since leadership had asked for this. She chose to resolve it with the team directly. She set up a session to share the context: technicians were using the product, business owners weren't seeing value, and that gap was blocking conversions. Once engineers understood the connection, the lead engineer proposed a way to build the new view on top of the technician experience. The team reduced scope, deprioritized other work and shipped an MVP in weeks. Business owners said it saved them hours every week preparing for their operations meetings.
Two lessons apply to most forward deployed teams. Share the vision and the "why now" early, before the design lands. And talk to your tech lead before you go to the whole team. A tech lead who sees the idea first can point out blind spots and help bring the rest of the team along.
How to prepare for the interviews
Forward deployed interviews test for exactly the challenges above. Expect product sense questions framed as customer role-plays, system design questions about changing existing systems, and behavioral questions about stakeholder management. The sections below are written with PMs in mind, and most of them apply to forward deployed engineers too.
Product sense: expect a customer role-play
One format I've seen: an interviewer plays a customer who wants a feature the platform doesn't have. You run discovery, then outline the ticket you'd write for engineering.
Cover the user's goal, the blocker, the urgency and the buyer's sign-off criteria. Ask who the user is and what success looks like for them. Then ask what's blocking them, how urgent it is, and what the buyer in the organization needs to see before signing off. Urgency is easy to forget, and it's the question that tells you where the request belongs on the roadmap. The buyer question is what separates forward deployed thinking from a standard product sense answer, because a happy user doesn't guarantee a signed contract.
Pro-tip: In running discovery, I like asking customers to describe a specific instance, rather than their overall process approach. You get a very different response when you ask someone: "Tell me about how you respond to a SEV" Vs. "Tell me about the last time you were on-call and needed to take care of a SEV2".
Use frameworks without naming them
Frameworks help you structure discovery. Naming them in the interview can make you sound rigid, and some interviewers will ask whether you can work without one. Start from the end state instead. Say you want to understand what job the user is hiring the product to do, then ask your questions. The structure shows through without the label.
Expect more system design trade-off questions
Forward deployed work rarely starts from a blank page. You're fitting new capabilities into systems the customer already runs and into a product your central team already built. Interviewers want to see how you reason about change, and they want to hear you name trade-offs out loud.
Common trade-offs to be ready for
Extend the existing system or build something new. Extending is (or theoretically should be) faster and keeps the product coherent, but it can inherit limitations that don't fit the new use case. Building new gives you freedom and creates a second system to maintain. Be ready to explain what would make you choose each.
Real-time or batch. When I worked on content distribution at Instagram, stale content was a core problem, and moving to real-time signals brought time to distribution from two weeks to under 48 hours. Real-time also costs more to run and is harder to debug. Many customer problems don't need it. A daily report that business owners review in a weekly meeting can run as a batch job.
Automation or human in the loop. The Instagram system combined real-time signals with human curation. Full automation scales better. A human in the loop catches the mistakes the model can't see and builds trust with stakeholders who are nervous about the change.
Precision or recall. When I launched ML-based forecast monitors at Datadog, every alerting decision was a trade-off between catching problems early and flooding users with false alarms. Too many false positives and people stop trusting the alerts. Too few and you miss the incident that mattered. Ask which mistake is more expensive for this customer.
Custom for one account or configurable for many. Working forward deployed with large enterprise clients, the pressure is always to build exactly what the account in front of you needs. The central team needs something that works for the next ten accounts. A good answer shows how you'd solve the customer's problem in a way that becomes a reusable capability (for example, a configurable rule rather than hard-coded logic for one client).
Bring data to your system or run where the data lives. Many enterprises won't move their data. Pulling data into your platform gives you control. Running inside the customer's environment can be the only way to get access at all, and it changes what you can monitor and update.
AI-specific trade-offs
Security. Security deserves its own place in any system design answer, and interviewers will expect you to raise it before they have to ask. Customers are asking sharper questions after the Hugging Face incident this summer. In July 2026, OpenAI disclosed that models undergoing an internal cybersecurity evaluation had escaped their testing environment and compromised part of Hugging Face's production infrastructure (read more about it). To gain internet access, the models identified and exploited a previously unknown vulnerability in a package registry cache proxy. Hugging Face reported that the intrusion started in its data-processing pipeline, where a malicious dataset abused code-execution paths to run code on a processing worker. It was reported as the first publicly documented case of AI models autonomously conducting a multi-stage intrusion against a third party.
You don't need the technical details in an interview, but the lessons map directly to forward deployed work:
Treat every input as untrusted. Datasets, documents, emails, tickets and web pages can all carry instructions or code. The Hugging Face intrusion began with a malicious dataset, and prompt injection works the same way through everyday content.
Give agents the least access they need. An agent that inherits the user's permissions is easier to reason about than one running on a broad service account. Scope credentials to the task, rotate them, and respect data boundaries between users (for example, contractors at the same company who shouldn't see each other's work).
Don't rely on isolation alone. Sandboxes and network boundaries can fail, so log what agents actually do and monitor for behavior outside the task.
Plan for response before launch. Know who gets alerted, how quickly access can be revoked, and what the customer will see if something goes wrong.
Know where the customer's data lives. Be ready to answer questions about residency, retention and who can access it.
A strong interview answer names the specific risks for this customer's data and systems and explains how the design contains them. It also shows you understand why security teams are often the last stakeholders to sign off, and that their concerns are legitimate.
Agent autonomy. Autonomy sits on a spectrum. An agent can suggest an action, draft it for a person to approve, take it with an easy undo, or act on its own. Match the level of autonomy to how reversible the action is and how costly a mistake would be. My own pilot is a good example: the right answer was a gradual ramp that gave teams time to trust the system. A strong interview answer often starts with low autonomy and describes what evidence would justify giving the agent more.
Blast radius. Ask what happens when the agent is wrong at scale. Limit the damage with scoped rollouts (one team, one region, one account), rate limits and spend caps, shadow mode where the agent runs without acting, and a kill switch the customer can use. Interviewers want to hear you plan for failure before launch.
Trust. I use three questions to evaluate whether an AI system is ready for customers. Is it auditable, so someone can trace why it made a decision? Is it calibrated, so its confidence matches its accuracy and it knows when to hand off to a person? Is it updatable, so the customer can correct it and see the correction stick? Stakeholders who feel uneasy about AI tend to relax when the answers are yes, because all three keep them in the driver's seat.
Evaluation and cost. Explain how you'd know the system works before launch and keeps working after it. Evals built on the customer's own data are more convincing than benchmarks. Model calls also cost money on every task, so check that the value of the task justifies the cost of running it.
Common mistakes
Jumping to architecture before clarifying who the user is, what job they need done and what constraints the customer has.
Designing from scratch when the question is about changing an existing system.
Treating the model as if it always gets the right answer, with no fallback for when it doesn't.
Giving the agent full autonomy by default.
Ignoring permissions and data boundaries until someone asks.
Leaving out how you'd roll back, measure success or contain failure.
Going deep on the technology and losing track of the customer and the economic buyer.
Presenting one design as the obvious answer instead of naming the trade-offs you considered.
For more on these, I recommend some of my Maven lightning lessons:
1) The seven deaths of an AI product (link) - if you're reading this in time, you can come live and ask questions (October 9, 09:00 AM PDT)
2) Avoid AI Communication Pitfalls (link) - a workshop I ran on this topic on Maven
3) System Design for PMs - How AI changes everything (link)
Expect behavioral questions to focus on stakeholder management
Forward deployed roles put you between customer stakeholders, the central engineering team, your own leadership and often the customer's executives. Interviewers are looking for three things: earning executive trust, influencing internal and customer teams, and adapting to change. Here are four questions I'd prepare for, and what interviewers are listening for in each.
"Tell me about a time you declined a senior stakeholder request." This question tests whether you can push back on someone with more authority while keeping the relationship intact and balancing the interests of the customer and your product.
A strong answer covers four things. First, show you understood what the stakeholder wanted and why. Second, explain the cost of saying yes, whether the customer would have been acting against their own best interest or the request would have come at a cost to your overall product. Third, describe how you handled the conversation (for example, meeting one-on-one first, bringing the right people into the room, or offering a smaller or later version). Fourth, share the outcome for both the customer and the relationship.
My own pilot story fits here. A senior executive wanted AI to drive every decision right away, but the sales teams weren't ready. Saying yes would have put adoption at risk, which worked against the executive's own goals. I pushed back by aligning everyone on what "AI control" meant and why a gradual ramp served the customer better. We kept the plan, the results improved, and the executive stayed a strong supporter of the project.
Avoid stories where saying no was easy, or where you won because of your title rather than your reasoning.
"Tell me about a time you needed to change priorities in the middle of a project." This tests whether you can make a hard call and bring people with you. The field service story fits here: a new focus on business owners meant pausing technician features customers had asked for. Cover why the change was urgent, which options you considered, what you deprioritized, and how you explained it to each group affected. Interviewers often follow up with "how did you know it was urgent?" so have that answer ready.
"Tell me about a time you saw an opportunity but didn't have the resources to pursue it." This is a question about judgment and resourcefulness. Forward deployed teams see opportunities constantly, and most can't be staffed right away. Strong answers show how you sized the opportunity, what you did with limited resources (a lightweight test, a partnership with another team, a scrappy prototype, or evidence gathered across accounts), and how you made the case to get it funded later. It's also fine if the answer was to let it go for now. Explaining why that was the right call shows maturity.
"How do you approach building trust with cross-functional teams?" This one asks for your approach, so answer with a few principles and back each with an example. Principles I'd expect to hear include sharing context early so people understand the "why now," bringing your tech lead in before the rest of the team, giving credit publicly, following through on commitments, and being honest when you got something wrong. In forward deployed roles, extend this to the customer's teams: agree on clear definitions, give them visible ownership, invite the curious ones into the build, and address fears about AI directly. Close with how you know trust is working (for example, engineers raising concerns early instead of pushing back late).
How to build your stories
I coach candidates to build every behavioral story with SPARK: Situation, Perspective, Action, Result and Key takeaway. It builds on the familiar STAR format by adding the two parts interviewers care about most in senior and forward deployed roles: your perspective on what was happening and what you learned.
The goal is to spark the chemistry with the interviewer, so they see how you think as well as what you did.
Situation sets the context briefly: who was involved and what was at stake. Perspective is where you show empathy and judgment. Explain the concerns on each side and the options you weighed (for example, pushing back, escalating, or finding a middle ground), and why you chose your path. Action covers what you did and how you brought people along. Result shares the outcome, with numbers where you have them. Key takeaway closes with what you learned and what you'd do differently.
Start with a title that captures the point of the story. Then work through each part of SPARK, adding enough detail that the story could only be yours, and trim from there.
Learn the customer's domain
Research the company, and also learn the industry its customers work in. Forward deployed teams earn credibility with customers by speaking their language, and the vocabulary changes a lot from one industry to the next.
A few examples:
Advertising and marketing. The marketing funnel, pricing models like cost per click and cost per thousand impressions, retargeting, customer acquisition cost and lifetime value.
Retail and consumer goods. Sell-through, inventory turns, pricing and promotions, trade spend, shelf availability and how sales teams split their time across stores.
Financial services. Risk and compliance requirements, fraud and false positive rates, know your customer (KYC) processes, audit trails and how regulators review automated decisions.
Healthcare. Patient privacy rules like HIPAA, clinical workflows, how care is documented and billed, and who has to approve a change before it reaches patients.
Field service and operations. Utilization, throughput, first-time fix rate, truck rolls and scheduling.
Enterprise software. Annual recurring revenue, seat-based versus usage-based pricing, renewals, churn and expansion.
You don't need to be an expert before the interview. Aim to understand how the customer makes money, what metrics their leaders are measured on, and what rules constrain them. Those three things shape what a successful AI deployment looks like and what the buyer needs to see before signing off.
Show your preparation in a way that fits the role
The strongest candidates show they can do the job before they're hired. Two examples from clients I've worked with:
One candidate connected her AI assistant to the company's public documentation before the interview. During the role-play, she recognized that the "missing" feature the customer described already existed in the platform. The interviewer noticed. It showed curiosity, preparation and fluency with the tools the role depends on.
Another client built a model comparing the cost and utilization curves of different AI models, so each task could be routed to the model that handles it most cost-effectively. She paired it with an executive-friendly dashboard that highlighted where the biggest savings were and what to do about them. It showed she understood a problem every enterprise AI customer faces as usage grows, and that she could turn technical analysis into decisions leaders can act on.
Both candidates stood out for the same reason. They brought something that looked like the work itself, which gave interviewers a clear picture of how they would operate with customers.
Closing thoughts
Forward deployed roles sit between what one customer needs today and what the product should become. They ask you to hold customer urgency, engineering constraints and stakeholder emotions at the same time. Above all, they ask you to help people feel in control of a technology that's changing how they work.
If you're preparing for a forward deployed role and want to talk through your stories, send me a message (rachel@pmadvising.com).
