Forward Deployed Engineering Is Back — And It Looks a Lot Like Consulting

TLDR: Google, OpenAI, and Anthropic are all ramping up demand for forward deployed engineers (FDEs), but the role is structurally shifting away from platform-building toward enterprise integration and AI rollout consulting. If you're an experienced engineer who loves building products, this might not be what you think it is.

There's a pattern emerging across the biggest AI labs, and once you see it you can't unsee it. Google is shortening its FDE interview process from four to six rounds over several weeks down to just two interviews in two days. That kind of urgency signals something — either an unusually high demand, an unusual willingness to compromise on hiring bar, or both. Meanwhile, OpenAI launched the OpenAI Deployment Company, a separate entity backed by $4 billion in private equity, and immediately acquired Tomoro, a UK-based AI firm with 150 FDEs spread across the UK, Asia, and Australia. Anthropic followed up with its own version — an unnamed standalone consulting company backed by Blackstone, Hellman & Friedman, and Goldman Sachs, focused on bringing Claude into "mid-sized companies across sectors."

The pattern here is deliberate and strategic. Both OpenAI and Anthropic are creating external companies rather than hiring FDEs directly into their core organizations. The implication is significant: FDEs in these new entities will likely receive equity in the deployment company, not in OpenAI or Anthropic itself. If the AI labs benefit enormously from FDEs accelerating enterprise adoption — and they will — the people doing that work won't share in the upside. That's the consultant model, plain and simple. You do the hard integration work, the lab collects the token revenue.

What does the actual job look like? The Google Cloud FDE job listing is worth reading carefully. The language is striking — phrases like "innovator-builder," "founder's mindset," and "white glove deployment" sound exciting, until you translate them. "Founder's mindset" means you'll get no spec and scope creep is your problem. "High-agency" means no resources besides yourself. "White glove" means you don't say no to customers even when you should. "Critical feedback loop transforming real-world insights into product roadmap" means you'll file tickets and hope a PM reads them. In practice, the job probably breaks down to roughly a quarter coding, half integration and plumbing work, and the rest meetings, customer hand-holding, and internal process overhead.

That said, this isn't necessarily a bad job — it's just a specific job. For early-career engineers who want Google on their resume but couldn't land a traditional SWE role, this is a real path in. Box CEO Aaron Levie made the point directly: career counselors should be steering CS graduates toward FDE roles now, because hundreds of companies across AI labs, SaaS vendors, and large enterprises are all going to be hiring for this skill set simultaneously. The FDE as a role isn't unlike what tech consultancies have offered new grads for decades — real-world, paid, fast-paced learning. The difference is that AI is the context, and agentic systems, MCP, and coding tools are the required fluency.

The demand trajectory is clear. AI labs want faster enterprise rollouts to drive token revenue. AI vendors want FDEs to close deals. Non-AI enterprises want to run their "AI transformation" without hiring a dedicated product engineering team. SaaS companies without AI products still want FDEs to get customers to production faster. All of these interests point the same direction: more FDE hiring. What's less clear is whether experienced engineers — people who've spent years caring about architectural quality, greenfield systems, and long-term product thinking — will find these roles satisfying. The honest answer is probably not. The FDE role, especially in these quasi-external consultant structures, is optimized for speed and customer delivery, not for building things that last.

Key takeaways:

Google, OpenAI, and Anthropic are all creating or expanding FDE programs, with the role increasingly resembling enterprise consulting rather than product engineering
OpenAI and Anthropic are housing FDEs in external, separately-funded companies, meaning FDEs won't get AI lab equity even if their work drives massive value
The actual day-to-day FDE role is roughly 25% coding, 50% integration and plumbing, and 25% customer meetings — not greenfield product work
FDE roles are likely a strong path for early-career engineers who want real-world AI experience; less attractive for experienced engineers who value engineering craft
Demand for FDEs is set to grow across AI labs, SaaS vendors, and large enterprises throughout 2026

Why do I care: The structural decision to house FDEs in external companies is the tell. When a company puts its highest-leverage customer-facing engineers into a separate entity with different equity, it's telling you something about how it values that work long-term. As a senior engineer or architect evaluating these roles, the question isn't just "is this interesting?" — it's "what does this career path look like in three years, and am I building leverage or just burning it?" FDE work in an external consulting arm is experience, yes, but it's experience that accrues to the lab's bottom line more than to your own trajectory. If you take one of these roles, go in clear-eyed about what you're trading.

The Pulse: Forward deployed engineering heats up again