Something strange has overtaken the engineering job market. Candidates spend hours tailoring cover letters and optimizing resumes, only for their applications to be parsed, scored, and filtered by an algorithm before a single human eye lands on them. On the other side of the screen, recruiters drown in thousands of AI-polished submissions that have been engineered to pass the very bots doing the screening. Both sides are locked in an automation arms race.
The result is a hiring landscape that many in the industry are starting to call, only half-jokingly, a hellscape: high-volume and deeply impersonal. But inside that dysfunction, a counter-movement is taking shape — one that is reshaping what engineering employers actually value, and raising urgent questions about equity, access, and what it really means to find talent in 2026.
Automated applicant tracking systems (ATS) are not new, but the rise of generative AI has dramatically escalated their influence, and the applicant response. Candidates now routinely use AI tools to tailor every sentence of a resume to a specific job description, inject the right keywords, and generate cover letters in seconds. The MEng program at the Fung Institute now explicitly teaches these skills because navigating this system has become a professional necessity.
The irony is not lost on hiring managers. Employers deployed AI to bring efficiency to screening: applicants responded with AI to survive screening. The signal-to-noise ratio has collapsed. What was supposed to democratize access to opportunity has, in many ways, done the opposite.
Evan James, CEO and founder of DreamWriter, a B2B hyper-personalization platform, sees the volatility from both sides. “You might have a cutting-edge idea of how to do something today,” he says. “But it might not be relevant anymore because of all the changes happening in technology one year from now.” For hiring, that instability is compounding: the attributes being screened for on a resume today may not be the ones that matter by the time someone starts the role. What does get through the filter? Often, the candidates who already had the structural advantage to begin with.
One of the more counterintuitive consequences of automated hiring is a renewed reliance on institutional brand. When individual applications are indistinguishable — all polished and keyword-optimized — employers have begun falling back on proxies they can trust. The name on the diploma is one of the oldest.
For graduates of programs like Berkeley’s MEng, this represents a structural advantage. The Fung Institute’s rigorous admissions process effectively pre-vets candidates. Their technical skills, leadership potential, and ability to operate in interdisciplinary teams are already validated. Employers increasingly know this, and many are doubling down on direct partnerships with a small set of trusted programs rather than sorting through a flood of AI-generated applications.
But the implication cuts both ways. A hiring market that privileges institutional pedigree is one that disadvantages candidates from less-resourced schools and non-traditional paths, regardless of ability. The efficiency gains of automation may be coming at the cost of the equity gains of the past decade.
Amid these changes, engineering leaders are remarkably consistent about what they’re looking for — and it’s not a perfectly formatted resume. Inga Backer, R&D Strategy Consultant at Siemens, puts it plainly: “Hands-on experience and strong portfolios often tell us far more than a perfect GPA. Capstone projects, internships, and real-world problem solving show how candidates think, collaborate, and approach ambiguity.” A great portfolio, she adds, “tells a story: what you built, why you built it, and the impact you had.”
Beyond the portfolio, Becker is looking for the qualities that truly differentiate candidates: quick learners who can collaborate across diverse teams, communicate complex ideas clearly, and navigate ambiguity without losing their footing. “Clear communication is as important as technical excellence, she says, “especially in global, interdisciplinary teams.”
Rama Ochieng Afullo is the founder and CEO of Satlyt, a startup turning satellites in space into virtual AI data centers. He is even blunter about credentials. “I don’t look at GPAs at all,” he says, “it’s not something that’s mattered to me ever. What is difficult is to translate that into practical skills in the workplace.” Satlyt is currently running two UC Berkeley capstone projects: one on small language models, one on hardware-in-the-loop testing for their space infrastructure. Afullo expects at least one or two of those students to become Satlyt employees.
If soft skills are the differentiator, AI literacy is quickly becoming the floor. Becker describes it as “essential across almost every engineering discipline — not because AI replaces engineers, but because it reshapes how they work.” Engineers who combine deep domain knowledge with software and data skills are increasingly in demand, and the ability to use AI to accelerate design, simulation of analysis is not a baseline expectation.
Afullo, who is himself a mechanical engineer who founded a software company, frames the stakes without softening them. “If you’re a software engineer without AI and ML skills, you’re already in a bad position,” he says, “You should just take it upon yourself to start learning those things — and this goes for everyone, every other kind of engineering, and folks outside the field entirely.” He counts himself as evidence. “I myself am a mechanical engineer, but I’ve founded a software company, which is not something I thought I’d be doing.”
James’ technical lead frames the same shift in operational terms, describing the biggest change in engineering as simply “being able to work with a lot more tools than you used to be able to,” including vibe coding and rapid prototyping. “The solutions you’re using,” James says, “isn’t necessarily what’s going to be relevant today, tomorrow, or the next day.” What all three agree on is that the continuous adaptation of new tools and technology, including AI, is what employers are actually hiring for.
If automated systems have degraded the value of the cold application, they have correspondingly elevated the value of the warm introduction. In-person recruiting events, industry conferences, alumni networks, and direct faculty referrals are all surging in importance, precisely because they provide the human verification that ATS systems cannot.
For MEng students, the Fung Institute’s explicit curriculum in personal branding, professional networking, and stakeholder communication represents a direct response to this reality. Teaching engineers to build and activate a professional network is about building core career infrastructure.
There is a harder conversation underneath all of this. The trends reshaping engineering hiring — automated screening, pedigree premiums, networking-as-strategy — do not affect all candidates equally. First-generation students, career changers, and graduates of less prominent programs face compounding disadvantages in a market that is simultaneously harder to enter and more dependent on existing social capital.
Becker’s parting advice is to go interdisciplinary: “The most exciting innovation happens at the intersections — software and hardware, AI and engineering, sustainability and digitalization. Don’t be afraid to go interdisciplinary.” That push to inhabit the space between disciplines may be the most durable career strategy in an era of rapid technological change. The engineers who will thrive are not those who perfected their resume keywords, but those who built things, connected ideas, and learned to work with and alongside AI without surrendering their own judgment to it.
The resume, in its current form, may indeed be dying. But the engineer who knows what they built, why they built it, and the impact it had will always find an audience.