A résumé can make hiring a forward-deployed engineer look easier than it is. An engineer who shipped an integration, a solutions architect, and a consultant with a cloud certification may describe different work using much of the same language. The descriptions can all be accurate and still leave you uncertain about what each person would do when a customer's operating conditions don't match the design.
In an a16z Show conversation published on January 29, 2026, Palantir chief architect Akshay Krishnaswamy and a16z's Erin Price-Wright discuss how forward-deployed engineering shapes hiring and product development. I find the conversation useful because it keeps the work in view, including the difficulty of building around a customer's actual problem. It raises a practical question for anyone designing an interview: how much opportunity does the candidate have to show how they respond when they learn something that changes the problem?
An engineer may arrive with a reasonable design and then discover that an operator knows something the brief left out. The operator may have less formal authority and considerably more context. I want to understand whether the engineer can examine that information and revise the design, including when doing so means explaining why their earlier answer no longer holds. An interview needs to allow for that kind of correction if we're going to learn anything about it.
At LockedIn Labs, the portfolio is a useful starting point for this discussion. A person can explain the work they owned and how it shipped using details they're authorized to share. Enterprise engineers often can't disclose the code, so the conversation has to respect that limit. If someone hasn't shipped into production yet, a clearly described practice project gives us work to discuss without implying experience they haven't had.
The remaining question is what happens when the context changes. A portfolio records a previous task, often after the person has had time to improve both the work and its explanation. I'd extend the conversation with an exercise that gives them a new operating constraint and enough room to respond to it. The following example is one way to do that, using a fictional setting so the discussion doesn't depend on access to a customer's information.
Give the person a warehouse and an assistant that answers dispatch questions from a supplied procedure. The initial design retrieves a matching product code, quotes the instruction, and prepares a dispatch recommendation. Ask the person to explain why that design is reasonable and record the assumptions it relies on before making any improvements.
Then the operator introduces a fact missing from the brief: the same product code exists at two sites, but one site's customer contract requires an additional release check. The shared procedure doesn't record that exception. The assistant could follow the procedure accurately and still recommend dispatching goods that shouldn't be released.
This gives the candidate a reason to investigate who owns the exception and which source establishes it. While that information is missing, they need to consider how much the assistant can reasonably do. Adding a more confident instruction to the prompt won't establish the operating rule, so I'd ask them to explain what information they need and how they'd obtain it. The operator should challenge the design respectfully and answer the questions prepared for the exercise, allowing the candidate to work through the correction without turning the interaction into a test of how much mistreatment they'll tolerate.
The revised design might require the site identity before retrieval, distinguish shared guidance from local release authority, and withhold a dispatch recommendation when the sources conflict. Retain the original version alongside the correction so a reviewer can follow why it changed. A case that would have passed before the correction but should now be refused helps make the difference explicit. Someone who is still learning to code can draw the workflow and explain those checks, while a developer can implement and run them.
Keep a record of the assistance available during the exercise. If an AI assistant proposed the site boundary, ask the person to explain and test that suggestion. If the interviewer supplied a hint, retain the hint with the work. Otherwise, a reviewer looking only at the final answer won't know which parts the person worked out independently. Agree the available tools, time allowance, and accessibility arrangements in advance, and record any changes to those conditions.
After the revision, introduce a third fictional site where the release rule is clear but the stock feed is stale. The earlier site filter won't resolve this problem, and the person needs to identify what can still be done without current stock information. Ask them to work through the case without supplying a solution and explain who should decide what happens next. Keeping their initial response and any subsequent correction lets a reviewer see whether they understood the earlier lesson well enough to apply it to a different constraint.
This proposed practice exercise is not a validated hiring instrument, and the example doesn't report a benchmark run. Our public benchmark methodology is also a draft, while the AI skills practice check helps people find a learning direction without issuing an official readiness rating. The exercise can make feedback more specific, but using it to predict job performance would require a separate validation study.
Even a strong attempt can't establish how someone will perform with a real customer or whether they're ready for an unsupervised production engagement. Differences in the help people received also limit comparisons, and an individual's record shouldn't give them credit or responsibility for work performed by a team. These limits need to accompany the result so a future reviewer understands what was actually observed.
Within those limits, the record can still help someone decide what to learn next. A reviewer can identify the fact that changed the design and discuss the reasoning behind the revision. If the person understood the site boundary but struggled with stale information in the next case, their next assignment can address that difficulty. Training becomes more specific because it's responding to something visible in the work.
In Show Me Your Portfolio, I develop the record a learner can build and discuss, and the AI delivery readiness guide offers broader starting paths. The exercise here gives that preparation a further step by asking another person to introduce context you didn't have when you began. You can then find out which parts of your reasoning remain useful and where you need to do more work.
For a company deciding how to organize AI implementation, I develop the wider argument in Everyone Has the Models. Faster at the Wrong Thing examines how delivery speed relates to deciding which problem is worth solving. These companion essays are published across the LockedIn Labs portfolio and shouldn't be read as independent endorsements of the benchmark.
To try the exercise, write down what the candidate will receive and prepare the operator's information before the session. Give them time to explain their decisions and to revise their work after the correction. A learner can use the same arrangement with a peer, keeping the feedback for a later attempt. That leaves both people with something concrete to discuss and a clearer idea of what the next exercise should cover.
Written September 6, 2026. Adapted for this public-draft field note and first published September 9, 2026. By Sam M. Sweilem, Founder & CEO of LockedIn Labs.
Source: a16z Show, How Palantir Scaled: Why the Best Software Is Built Backwards, Akshay Krishnaswamy with Erin Price-Wright, January 29, 2026; source identity and publication date checked September 9, 2026. The proposed exercise and its interpretation are ours. Palantir and a16z have not endorsed or reviewed this methodology.
AI-assisted source checking, editorial adaptation, and software production were used.