Last week I posted twice on LinkedIn about Forward Deployed Engineers. This is the long version, with live numbers from Euro Top Tech.
I work on this stuff full time (1:1 coaching plus Euro Top Tech), so I hope it’s useful to some of you!
Let’s start:
1. What is an FDE, and who are the cousins
FDE stands for Forward Deployed Engineer: an engineer who is “deployed forward”, at the customer. You sit with them (often literally, in their office), understand their systems and their data, and build on top of your company’s product, inside the customer’s stack.
Half software engineer, half consultant, with more customer meetings than a regular engineer.
Palantir made the title famous. In 2025 and 2026 the AI labs and the data platforms copied it, because selling AI to a large company turned out to be 20% model and 80% integration: messy data, legacy systems, permissions, people.
The cousins:
SE, Solutions Engineer: demos, proofs of concept and implementations, usually attached to a sales team.
PE, Product Engineer: the title AI-native startups use for a full-stack engineer who owns a product area end to end, talks to users and ships.
Adjacent: Solutions Architect, Sales Engineer, Solutions or Implementation Consultant, Customer Engineer.
Deep down, they all share the same core skillset:
Know about tech
Be able to build things with cloud/AI
Have decent social/communication skills
2. What I see in my coaching program
AI Engineer: hottest title in 2025
FDE/SE/PE: hottest titles in 2026
I have now someone in my coaching program with some good experience as an FDE (like 1 or 2 years), after a few years of fullstack/AI experience, and he’s cleaning up.
Easiest profile to land interviews at top companies right now.
Same thing for Solutions Architect/Consultant/Engineer.
3. The data: FDE roles live in Europe right now
Counted on the two Euro Top Tech job boards (big tech and well-funded scale-ups, onsite/hybrid plus fully remote, Europe only) on 19 October 2026, by job title:
About 230 live postings carry “Forward Deployed” or “FDE” in the title. 171 of them are engineer roles. The rest are managers, technical program managers, product managers and even recruiters for FDE teams: companies are building departments, it’s not just an “experiment”.
37 companies. The biggest hirers: Salesforce (37 roles), Databricks (19), Palantir (14), Google (12), Happyrobot (10), JetBrains (9), OpenAI (9), Cloudflare (6), Mistral AI (6), Cohere (5), MongoDB (5), Snowflake (4), Anthropic (3), Microsoft (3). Then Adobe, Atlassian, Amazon, Lovable, ElevenLabs, n8n, Nebius, LiveKit, Cresta, Coupa and a long tail of AI startups. They ALL pay a lot.
Where: United Kingdom 39, Germany 18, Ireland 16, France 11, Poland 11, Spain 8, Netherlands 6, Switzerland 5, Italy 5, Sweden 4. Plus 37 remote roles, 27 of them open to anywhere in Europe.
55 of the 171 were posted in the last 30 days.
Seniority: mostly mid-level and senior, 19 staff/principal, and a few intern and new-grad postings.
For comparison, “AI Engineer” appears in 90 live titles and “Machine Learning Engineer” in 80. The FDE title alone (171) now matches AI Engineer and ML Engineer combined (170).
And FDE is the small cousin. The whole customer-facing engineering family on the boards today:
Solutions Architect: 394
Solutions Engineer: 239
Solutions / Implementation Consultant: 138
Sales Engineer: 98
Product Engineer: 32 (small, but it’s the title Wise, OpenAI, Framer, Attio, Linear and n8n use for their full-stack hires)
Around 1,100 live roles in total.
These counts are by title, but it’s in practice even more: plenty of “Software Engineer, Applied AI” postings are FDE work under another name.
One interesting detail: Salesforce is hiring “Forward Deployed Engineer, Dutch/German speakers” and “French/Arabic speakers” in Dublin, and a “Spanish speaker” in Madrid. Which brings me to the three things that matter more than the title.
4. The title matters less than these 3 things
1. Positioning
Make your past experience aligned to your target roles and companies.
Brand your CV and LinkedIn so that they resonate with these roles and, ideally, attract recruiters.
In practice: implementations delivered, customers you sat with, systems you glued together, ambiguity you survived. Those are the lines an FDE hiring manager scans for.
2. Underlying skillset
Tech capabilities, communication/social skills, pragmatic/product-minded/business-aware orientation.
3. Location
Connected to positioning/strategy: when you’re dealing with customers, geographical markets matter.
With a series of implications: culture, languages, economic regions, contacts/network, etc.
The language-specific Salesforce postings above are the obvious example. Speaking German or French is worth more in this family than in a backend team.
5. Where AI fits in all this
AI has become a nice to have / must have across roles. Rather than a role-defining skillset. (More on this in the 2030’s Survival Guide for Tech Workers in Europe.)
You should be able to use it to 10-100x your productivity, build systems and shape products (often, also AI products/systems).
There’s still a path for MLE/AI Engineers too, but it’s a vertical, and increasingly often regular engineers are expected to do that job too.
The FDE role is where this is most visible: the job is to make AI work for one specific customer, with their data and their constraints. A model nobody can deploy makes nobody money.
6. The catch
Between a solutions engineering role and an FDE role, solutions engineering is the better lifestyle. FDE is more onsite and more grind.
The data says the same: of the 171 FDE engineer roles, 114 are onsite, 10 hybrid and 47 remote. “Forward deployed” often means the customer’s office, with travel on top.
You also work on someone else’s stack and someone else’s deadlines, and you’re the person in the room when the demo breaks. Some engineers love exactly that. Many don’t. Know which one you are before you rebrand your CV.
7. How to go for it
Target this job category deliberately. Pick the lane per company: some hire Solutions Engineers, some FDEs, some both and make you choose. Have a plan A and a plan B based on interests and skills.
Rewrite your CV and LinkedIn in the family’s language (point 4): not just an “unfocused engineer”, but what the FDE ads are asking for.
Research the interview loops before you prep. They are newer than big-tech SWE loops and they vary: practical coding, AI-literacy questions and customer-scenario rounds, fewer pure algorithm and data structures puzzles. Evidence of AI projects in production and of user-facing work is what gets probed.
Build the evidence: one or more real integrations for one or more real user/customers from your work history.
Referrals matter more in small teams, and FDE teams are small. Check out this guide to learn how to go about this.
I hope this helps someone out there navigate this challenging market!
Browse the live roles (members see the company and the apply link):
If you want help positioning for this up and coming jobs, check out my 1:1 coaching.
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Oh, we have some FDEs where I work, they produce unmaintainable code and software, and spend on tokens more than it would take to hire a small team of specialists.