Fake engineering candidates are getting harder to catch. Learn the red flags at every stage and build a vetting process to stop fraud before an offer goes out.
The Candidate Who Doesn’t Exist
Picture this. A senior backend engineer clears your technical screen. Strong CV. Relevant healthtech experience. Confident system design answers. The video interview goes well, although the candidate’s camera lags a little.
Three weeks after starting, the work doesn’t match the interviews. Code reviews reveal gaps a senior engineer wouldn’t have. A colleague notices the person on the stand-up looks slightly different from the person in the final round.
This scenario sounds extreme. It’s becoming common.
Gartner predicts 1 in 4 candidate profiles worldwide could be fake by 2028. In a survey of 3,000 job candidates, 6% admitted to interview fraud, either posing as someone else or having someone else pose as them.
Engineering roles sit at the centre of this problem. They pay well, they’re often remote and they come with access to systems, code and data. For a healthtech startup, a fake hire is not only a bad hire. It’s a security incident with access to patient data.
This guide covers:
- Why engineering roles attract fraud
- The four types of candidate fraud to watch for
- The red flags at each stage of your process
- A step-by-step vetting process to stop fraud before an offer goes out
Why Engineering Roles Attract Fraud
Fraudsters follow money and access. Engineering roles offer both.
Remote work opened the door. Dawid Moczadlo, co-founder of Vidoc Security Lab, put it plainly: “Remote jobs unlocked the possibility of tricking companies into hiring fake candidates.”
AI made fraud cheap. Researchers at Palo Alto Networks found someone with no technical experience built a convincing deepfake job candidate in about 70 minutes, ready for live video interviews.
The problem is already visible to hiring teams. A Greenhouse survey of 4,136 hiring managers found 31% had interviewed a candidate they suspected, or confirmed, was using deepfake technology. 62% said candidates are now better at faking it than recruiters are at catching it.
Some of this fraud is organised. Fake candidates with ties to North Korea have drawn significant headlines, and security experts describe hiring candidates from sanctioned nations as a national security concern.
The cost goes beyond a wasted salary. Jamie Kohn, senior research director at Gartner, warned candidate fraud creates cybersecurity risks “far more serious than making a bad hire.”
Why Healthtech Faces Higher Stakes
Every company pays a price for a fraudulent hire. Healthtech pays more.
A new engineer at a healthtech startup often receives access to:
- Production systems containing protected health information
- EHR integrations and client credentials
- Security controls covered by HIPAA and enterprise contracts
- Source code representing years of regulated product work
A fraudulent hire with this access creates exposure on several fronts. A potential data breach. A failed customer security audit. A broken enterprise contract. Reputational damage with the health systems you depend on.
Healthtech startups also tend to run lean, remote engineering teams. Fewer people means fewer chances for someone to notice something feels off.
Vetting is part of your security posture, not an HR formality.
The Four Types of Candidate Fraud
Fraud comes in different forms. Each one needs a different defence.
1. Fabricated experience
The person is real, but the CV isn’t. Invented employers, inflated titles, fake projects or work history copied from someone else’s profile. AI makes polished fabrication easy.
2. Proxy interviewing
A skilled engineer sits the interviews. A different person turns up for the job. Sometimes the proxy is a friend. Sometimes it’s a paid service.
3. Deepfake identity
Real-time face or voice manipulation during video interviews. The person on camera isn’t who they claim to be, and sometimes doesn’t exist at all.
4. Live AI assistance
A real candidate feeds interview questions into an AI tool and reads the answers back. Data from Fabric, an AI interview company, found cheating rates in software engineering interviews hit 48%, compared with 12% in sales. Treat vendor figures with care, but the direction is clear.
One important distinction. Using AI to write a CV is not fraud. Gartner found 4 in 10 candidates use AI during the application process, mainly to write text for their CV, cover letter or assessments. A polished CV is no red flag on its own. Misrepresenting who you are or what you’ve done is.
Red Flags at Every Stage
Use this checklist across your process. One flag alone rarely proves fraud. Several together deserve a closer look.
CV and profile stage
- A LinkedIn profile created recently, with few connections and little activity
- Employment history impossible to verify, or employers with no web presence
- Generic project descriptions without specific systems, numbers or outcomes
- Contact details which don’t match the stated location
Screening call
- Reluctance to turn on a camera, or repeated “technical issues”
- Answers which sound rehearsed and fall apart with a simple follow-up
- Vague answers about former colleagues, managers or team structure
- Unusual delays before answering, as if waiting for something
Technical interview
- Strong answers to standard questions, weak answers to unscripted ones
- Eye movement suggesting reading from another screen
- Inability to explain the reasoning behind their own solution
- Lip movement out of sync with audio, or visual glitches around the face
References
- References reachable only through contact details the candidate supplied
- Referees who can’t describe specific work the candidate did
- Several references with very similar wording
Offer and onboarding
- A request to ship the company laptop to a different address from the one given
- Changes in voice, appearance or communication style after the start date
- Identity documents which don’t match the person from the interviews
How to Build a Vetting Process Which Works
You don’t need to turn your interviews into an interrogation. You need a few deliberate checks at the right points.
Step 1: Verify identity early
Confirm identity before investing serious interview time. Ask for government ID at the screening stage and match it against the person on camera. Doing this early protects your engineers’ time as well as your security.
Step 2: Build unscripted moments into every interview
Fraud thrives on predictable questions. Ask candidates to go deeper on their own past work:
- “Draw the architecture of the system you built at your last company.”
- “What would you change about it now, and why?”
- “Who disagreed with your approach, and how did you resolve it?”
Real engineers enjoy these questions. Fraudulent candidates struggle, because the answers don’t exist in a script.
Step 3: Verify employment independently
Never rely only on the contact details a candidate provides. A verifier who finds the employer’s number independently dismantles fake references and shell-company job histories in a single phone call.
Step 4: Test depth with live follow-ups
Push past the first answer. Ask “why” twice. Change a constraint mid-problem and watch how the candidate adapts. Someone reading from an AI tool struggles when the question keeps moving.
Step 5: Add one verified in-person or high-trust step
For senior or high-access roles, add one in-person meeting or a verified video session with ID checks. Honest candidates don’t mind. Gartner found 62% of candidates were more likely to apply when a role required in-person interviews.
Step 6: Check again at onboarding
Fraud doesn’t stop at the offer. At onboarding:
- Match ID to the person on the first day’s video call
- Ship equipment only to a verified address
- Grant system access in stages, starting with the least sensitive
- Ask the hiring manager to flag any change in voice, appearance or ability
Step 7: Set clear rules on AI use
Tell candidates which AI use is acceptable and which isn’t. Gartner advises organisations to define acceptable AI use and make candidates aware of fraud detection mechanisms and potential legal consequences. Clear rules deter fraud and protect honest candidates from unfair suspicion.
Don’t Overcorrect
Fraud is real. So is the risk of treating every candidate as a suspect.
Strong engineers have options. A process full of suspicion and friction pushes them towards companies offering a better experience. The goal is not more hurdles. It’s smarter ones.
A few principles keep the balance right:
- Explain why you run identity and reference checks. Most candidates respect it.
- Keep checks proportionate to the role’s access level.
- Focus interview questions on depth and reasoning, which serve both fraud detection and quality of hire.
- Move fast once a candidate passes. Rigour and speed work together.
The best vetting process doesn’t feel like vetting to an honest candidate. It feels like a well-run interview.
Protect the Team You’re Building
Fake candidates are no longer rare. AI has made fraud cheap, remote hiring has made it easier and engineering roles make it profitable.
For healthtech startups, the stakes are higher. A fraudulent engineer with access to patient data is a breach waiting to happen.
The fix is straightforward:
- Verify identity early
- Ask questions only real experience answers
- Check references independently
- Stage system access after the start date
These steps take little time. Skipping them risks far more.
Hiring senior engineers with access to sensitive systems? Synaxia Group places senior software and product engineers into healthtech, medtech and biotech startups. Every candidate we present goes through identity, employment and technical depth checks before they reach your interview loop. Get in touch with Synaxia Group to discuss your next hire.