Technical Skills Verification

Verification skills in AI era : AI has collapsed the skill floor for verification coding. Any engineer with a browser can produce correct SystemVerilog now. The syntax question is settled. It’s free. So what do you actually ask in a verification interview in 2026? The shift I keep landing on: the value of a verification engineer was never in writing the code. It was in knowing what to verify and why. Here’s what I’m actually screening for now: Coverage forensics: Don’t write me a covergroup — any LLM can do that. Instead, look at this 98% coverage report and tell me why I should still be nervous. The missing 2% might be the exact cross between low-power entry and outstanding coherent transactions that causes silent data corruption at a customer’s data center. The metric isn’t the skill. Reading through the metric is. Failure scenario thinking: You’re verifying a DMA engine sharing an AXI bus with a real-time safety processor. The spec says nothing about arbitration under back-pressure. What do you do? I’m listening for whether the candidate recognizes that the ambiguity is the bug — and has the instinct to build a test for something nobody asked them to test. Debug root-causing: AI can help you write tests. But when a simulation fails at cycle 4.2 million, can you trace it back through five levels of hierarchy to a single FSM corner case? Debug is pattern recognition built on years of watching silicon break. It’s the difference between someone who runs simulations and someone who actually finds bugs. Verification planning strategy: Knowing when to deploy formal vs simulation vs emulation vs FPGA prototyping — and why. A cache coherency protocol might need formal. A video pipeline might need emulation for real-frame throughput. AI can execute any of these. Signoff conviction: Would you hold a tapeout? When the schedule says go but your gut and your coverage holes say wait — do you have the spine and the data to make that call? MediaTek’s Dimensity, Apple’s M-series, Qualcomm’s Snapdragon — none of these tape out on syntax correctness. They tape out on whether the verification team had judgment and conviction. AI makes the average verification engineer more productive. It doesn’t make them more thoughtful. And in verification — where our job is imagining failures the design team didn’t — thoughtfulness is the product. If you’re still asking candidates to hand-code a UVM agent on a whiteboard, you’re testing a skill that costs $0 and takes 4 minutes. If you’re hiring for ASIC verification right now — what’s the one question you’ve added or dropped because of AI? Genuinely curious how the industry is adapting. #VerifWord #ASICVerification #Taalas #AIChips #VLSI #EDA #ChipDesign #FormalVerification #SemiconductorDesign #AIInference

FEEDBACK NEEDED: What Data Should Be Included in Open Badge 3.0 to Support Verification and Validation of Skills? As employers, educators, and workforce systems increasingly shift toward skills-based hiring and advancement, Open Badge 3.0 (OBv3) provides a vital standard for issuing verifiable, portable, and machine-readable digital credentials. To ensure badges support trustworthy validation of skills, the following data elements are essential: ✅ Core Metadata for Identity and Trust Issuer Identity: Verified organizational metadata (e.g., legal name, credential registry ID, website) to authenticate source. Recipient Identity: Cryptographically linked (not publicly exposed) identifier ensuring badge belongs to the verified individual. Issue and Expiry Dates: Timestamped evidence of when the badge was earned and if/when it expires. 🛠 Skill Evidence and Validation Competency Frameworks: Align the badge to recognized skill/competency frameworks (e.g., ESCO, O*NET, Credential Engine). Assessment Description: Clear articulation of how skills were evaluated—exam, performance, portfolio, etc.—and by whom. Demonstration Evidence: Link to artifacts or media (e.g., project, video, rubric) showing real-world skill application. Level of Proficiency: Indicate depth of mastery using taxonomies like Bloom’s or CEFR (if applicable). 🔗 Transparency and Interoperability Credential Registry Links: Direct connection to authoritative registries like the Credential Engine for transparency, comparability, and validation. Metadata Standards: Conform to schema.org, JSON-LD, and IMS Global/1EdTech standards for machine readability and system integration. Verifiable Claims: Use cryptographic signatures and tamper-proof digital wallets to ensure authenticity. 📊 Learner Context and Use Related Pathways: Reference how the skill connects to education, career, or industry pathways. Alignment to Job Roles: Include job role tags (e.g., from O*NET or SOC codes) where skill is commonly applied. Endorsements: Validation from third-party employers or industry groups strengthens badge credibility. --- Summary: To make Open Badge 3.0 a trusted mechanism for verifying and validating skills, it must include structured, transparent, and portable data—who issued it, what it represents, how it was earned, and how it connects to real work. This is essential in the age of AI-driven hiring and skills-based opportunity.

🤖 AI has been causing issues with our recruitment. Almost ironic for a data and AI tech company... 😲 As a smaller startup, finding the perfect candidate is important—very important. A bad hire hits us where it hurts—finances and time. Two things that we don't have the luxury of being able to absorb endlessly. 💵 ⏱ This is why effective technical screening is paramount. But with the rise of AI and the prevalence of powerful LLM's at everyone's fingertips, properly verifying technical skills is getting harder. For our analytics roles, we traditionally used "take-home" tests, which are scalable but vulnerable to cheating, especially in the age of AI and LLMs. We used to run these in Round 2 - after a first interview round. In our new AI enabled world, how can we scale our technical tests in a way in which we can trust the results? Quite the conundrum. 🤔 👨💻 What I trialled: We've redesigned our analytics hiring process to balance scalability with reliability, optimising each stage to ensure thorough evaluation without sacrificing efficiency: - Round 1: We moved the "take-home" technical test to the first round screening step, allowing us to efficiently evaluate a large pool of candidates. - Round 2: This interview stage now begins with a brief live technical assessment to confirm the authenticity of the results from the first round. By moving the technical test to the first round, we leverage its scalability and reduce the load on subsequent rounds. We then leverage a small part of Round 2 to verify the technical score - just the first 10 minutes until the interviewer is comfortable that the score matches what they are experiencing and the Round 1 score can be trusted. 📊 This new strategy significantly streamlined our process: - 46 candidates took the initial test; 37% were eliminated, lightening the load for subsequent rounds. - 10% of the remaining candidates in Round 2 failed to demonstrate on-the-spot skills, revealing discrepancies between their test scores and actual abilities - including one case where the candidate was actively using ChatGPT during the live interview! 🤯 🎉 The result? A fantastic new hire who truly fits our team's technical and cultural needs! As AI continues to transform the landscape of technical recruitment. How is your team ensuring the integrity of your hiring process? Share your stories and tips below! TalentFirst.ai #ai #artificialintelligence #futureofwork #recruitment #culturate #tfai

Catalina S. told me something that completely reframes how we should think about skills validation. After 10+ years leading workforce transformation at Vodafone, T-Mobile, and DataCamp, she dropped this truth bomb during our latest Business AI Playbook episode: "Companies don't just want employees to know things, they want employees who can do things." Most L&D teams are still stuck measuring completion rates and quiz scores. But Catalina's seeing something different work: Evidence-based skill validation that proves real-world capability. Here's what she's implementing right now: → AI-powered surgical feedback — Johns Hopkins is using AI to analyze actual surgical videos, providing objective feedback on technique and precision, not just theoretical knowledge → Peer-led GenAI Scouts — A global engineering org turned employees into instructional designers, achieving 90% engagement and 20-40% time savings on repetitive tasks in just 6 months → Real-world retail simulations — AI roleplay environments where new hires practice customer interactions, earning badges only after demonstrating 3 successful and 3 unsuccessful scenarios with lessons learned → Skills data as strategic inventory — Finally giving companies visibility into their actual internal capabilities while supporting employee growth aspirations Catalina's challenge to every L&D leader: "We need to shift from knowledge retention to evidence-based skill validation." The companies getting this right aren't just improving training metrics. They're fundamentally changing how their workforce approaches capability development. 🎥 Watch the full conversation below 🔄 Share this if you think proving skills matters more than passing tests What's the most creative approach you've seen to validate real-world skills? #BusinessAIPlaybook #LearningInnovation #SkillsValidation #AITransformation #FutureOfWork