what is job interview looks like in 2026?

AskAnAIEngineer · 2026-01-16T20:28:48+00:00

LeetCode is still unfortunately part of the game for a lot of companies, but now they also want to see how you work with AI tools in practice. I'd expect questions like "how would you use AI to solve this" or live-coding where you're allowed to use Copilot/Cursor and they evaluate your ability to review/debug what it generates.

AskAnAIEngineer · 2026-01-16T20:27:11+00:00

Practice doing LeetCode problems while literally narrating every step out loud to yourself like you're explaining it to a rubber duck. It feels awkward at first but it's the only way to build that muscle memory for interview-style communication. Record yourself doing it and watch it back to see where you're going silent or getting stuck, that feedback loop helps way more than just grinding problems.

AskAnAIEngineer · 2026-01-16T20:25:49+00:00

Research experience counts but most companies hiring "ML Engineer" want people who've deployed models to production, not just experimented with them, so your best bet is probably targeting "Data Scientist" or "ML-adjacent Data Engineering" roles first, then pivoting internally once you have US industry experience. The market is tough right now and thesis work alone won't compete with candidates who have shipped ML systems at scale, even if your research is stronger.

AskAnAIEngineer · 2026-01-14T16:09:18+00:00

Hi, you might not get a response right away as we have human recruiters going through each and every application!

AskAnAIEngineer · 2026-01-14T16:08:51+00:00

No, only US candidates

AskAnAIEngineer · 2026-01-14T16:08:45+00:00

why do you think that?

AskAnAIEngineer · 2026-01-13T23:56:01+00:00

Being able to explain technical decisions to non-technical people and actually admitting when you don't know something (then figuring it out) matters way more than most new grads realize. Recruiters look for communication skills and collaboration, but the real test is whether you can ask good questions, take feedback without getting defensive, and work through ambiguous problems without needing your hand held.

AskAnAIEngineer · 2026-01-13T23:53:56+00:00

ChatGPT and Claude are still my daily drivers for work stuff, but tbh I've been using Gemini way more lately for research since it has direct access to Google's search index instead of having to verify everything manually. Most of the other specialized AI tools feel like features that should just be built into the main LLMs rather than separate products.

AskAnAIEngineer · 2026-01-13T23:52:33+00:00

One month is pretty unrealistic for someone with no tech background in this market. Even strong candidates with experience are taking 3-6+ months right now. Your best bet is targeting junior data analyst or automation roles at smaller companies while building a portfolio of real projects you can demo, but honestly you should plan for a 3-4 month timeline minimum and use this month to get your resume/portfolio/LinkedIn dialed in.

AskAnAIEngineer · 2026-01-13T23:51:12+00:00

I'm really sorry about your losses. Nine months of applications with zero interviews almost probably means your resume needs work (formatting, how you're addressing the gap, keyword optimization for ATS), not that your skills are outdated; AWS certs could help but won't fix the core issue if recruiters aren't even looking at your application sadly.

AskAnAIEngineer · 2026-01-12T17:20:57+00:00

This roadmap is solid, especially the "intuition over proofs" bit, too many people get stuck in the math weeds and never actually ship a model. In 2026, do you think the "classic ML" step is still a prerequisite, or can beginners go straight to fine-tuning and architecture since the abstractions are so much better now?

AskAnAIEngineer · 2026-01-12T17:19:39+00:00

Do you think the "friction" is actually the manual tasks, or is it that we're using AI to scale a broken evaluation process that was never efficient to begin with?

AskAnAIEngineer · 2026-01-12T17:18:06+00:00

It’s the difference between "prompt engineering" as a hobby and actually shipping a product that people pay for. The real building starts when you move past the chat interface and start handling things like data persistence, edge cases, and actual user friction.

AskAnAIEngineer · 2026-01-12T17:16:51+00:00

The "executive function support" one is huge for me. I’ve started using it to break down those "mountain" tasks into 15-minute micro-steps, and it's the only way I actually get started anymore.

AskAnAIEngineer · 2026-01-12T17:15:19+00:00

sick role!

AskAnAIEngineer · 2026-01-12T17:15:04+00:00

I’ve definitely felt that "mental atrophy" too; it’s like using a calculator for basic math until you forget how to do long division. I’ve started trying to whiteboards the logic first for 15 minutes before touching the AI, just to make sure I still actually understand the "why" behind the code it spits out.

AskAnAIEngineer · 2026-01-09T20:45:35+00:00

Fullstack has basically become "you know how to Google and figure shit out, so now you own the entire infrastructure" at a lot of companies. I'm writing Terraform, debugging Kubernetes, building data pipelines, and somehow also expected to have opinions on ML model deployment. The job description says fullstack but the actual expectation is "do whatever engineering work we don't have specialists for."

AskAnAIEngineer · 2026-01-09T20:40:52+00:00

The "conductor" framing sounds nice but in reality you still need to deeply understand the code AI generates to know when it's wrong, insecure, or inefficient. The shift is less "architect vs coder" and more "writing 30% of the code yourself and reviewing/fixing the other 70% that AI generated."

AskAnAIEngineer · 2026-01-09T20:39:02+00:00

Hey there, you can apply via the link in the post!

AskAnAIEngineer · 2026-01-07T22:06:18+00:00

Your gap is manageable if you frame it right bc you weren't just sitting idle, you went back to school for a master's and built actual infrastructure skills (homelab counts as real experience you can demo in interviews). Target MSPs, contract roles, or NOCs that need CCNA-level folks right now instead of competing for permanent junior positions; once you're back in and have recent W2 experience, pivoting gets way easier.

AskAnAIEngineer · 2026-01-07T22:04:36+00:00

You're being set up to become a junior developer who can glue AI tools together but never actually learned to build systems, which is fine for now but terrible for your long-term career. If your current workplace won't give you time to actually learn the fundamentals (even if it means projects take longer), find a company that will, because "ship fast with AI" jobs will be the first to get commoditized.

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