r/ModernHiring

Is it slow communication, too many interview rounds, unclear expectations, salary mismatch, or something else?

Nothing huge just one change that would make the process feel more straightforward or respectful from a candidate’s perspective.

With so many listings asking for different skills and experience, what makes you think, “Yeah, this one is actually worth applying for”?

I am learning engineering hiring at small software companies and I am trying to understand where the process is disturbed and breaks. mainly tech recruiters please share your experience. And this would seriously help me I have been talking to recruiters and engineering managers about how the engineers or developers hiring gets hard or like what processes or challenges they go through to find best engineers. And share anything that is making your hiring disturbed.

1. Is the hardest part finding qualified candidates? or knowing whether someone actually has the technical ability they claim?
2. Screening hundreds of applications? or getting good candidates to respond? How do you make your hire a success.

Something else? I'm especially interested in what takes the most manual time or causes the worst hires.
Not selling anything, I'm trying to understand the problem from people actually doing the hiring. Even small experience or one line about this can help me a lot.

How are recruiters current handling a huge influx of applications for a job role?

Which tools are best for screening candidates?

Hi, i am looking for diversity hiring options but i cant find any reliable sources, i have heard that IBM heirs via diversity hiring, is that even true, i need to get a job in tech
so please help me out if any of you know anything about it??
thank you :)

Hello, I am currently building an HR tech/horing intelligence product/platform based on the market research I have done and the data collection from different platforms about the current hiring world problems but I want to check with the community again, what is the best solution that will help the job seekers in this harsh job market where finding a job is a hassle? Is it something related to referrals or fixing the ghosting problem or any other solution where the job seekers are benefitted while applying for jobs?

I have been brainstorming myself but based on the recent posts here I would like to check with the right people.

I’ve noticed that hiring seems to have gotten more complicated lately.

Some jobs ask for a huge list of skills and experience, then candidates have to go through multiple interviews and assessments just to hear nothing back.

Are companies expecting too much from candidates now, or is this just part of how hiring works?

What’s one thing about the hiring process you think needs to change?

Is it salary transparency, clear timelines, better communication, fewer interview rounds, or something else?

Something I have spent a lot of time on lately, sharing here for feedback from other founders who hire.

Post one role and you get hundreds of applications. Inflated resumes, borrowed experience letters, skills listed that nobody can actually back up, and now a wave of AI-written profiles on top. There is no fast, reliable way to tell who can really do the work versus who just wrote that they can. So it becomes hours of screening calls that go nowhere, or hiring on gut and a familiar company name on the resume, and sometimes finding out months later that half the resume was fiction.

The core issue is that hiring still runs on self-declared claims. The burden of proof sits on the employer, one interview at a time, and background checks catch it too late and cost too much.

So I have been building toward a different model, where a candidate's skills, work history and projects come backed by real records and approved evidence instead of self-declaration, so the claims are verified before a profile ever reaches an employer. Verified, not claimed.

What I would really value from this community: does this match what you see when you hire? Where does the unverified-candidate problem hurt you most, screening time, bad hires, background-check cost, something else? And for those who have hired at scale, what actually gave you confidence in a candidate before you committed?

I’m looking for some honest, blunt advice from recruiters, hiring managers, senior engineers, or people who have successfully landed AI/ML roles in the US market.
I’m currently job hunting for AI/ML Engineer, Machine Learning Engineer, and Generative AI/Agentic AI roles.
The confusing part is that I am getting recruiter attention, but I’m repeatedly getting rejected after screening/interview stages.
Over the past few months, I’ve received screening/interview opportunities with companies including EY, Ford, TikTok, Nimble Gravity, Goldman Sachs, Capital One, and several others.
So I don’t think my biggest problem is getting my resume noticed.
The problem is converting those opportunities into the next round or an offer.
For example, with Goldman Sachs, I went through their coding process. I passed the coding-related stages, solved the problems, and explained my approach and code during the interview. I still ended up receiving a rejection.
I’ve had similar experiences with other companies where I felt the recruiter/screening conversation went reasonably well, but I was rejected afterward.
At this point, I’m trying to identify whether there is a recurring problem in my interviewing that I’m not recognizing myself.
I would really appreciate advice on:
What are recruiters actually evaluating during an initial screening?
If my technical answers are correct, what else could cause a rejection?
How important is communication during technical interviews?
How do I know if I’m giving answers that are too long, too technical, or not detailed enough?
For AI/ML/GenAI roles, what do hiring managers actually want to hear when discussing previous projects?
How important is it to clearly explain what I personally built versus what the overall team built?
If I solve a coding problem correctly and explain my solution, what could still cause a rejection?
Could my resume be creating expectations about my experience that I’m not demonstrating effectively during interviews?
How should I talk about experience with LLMs, RAG, Agentic AI, LangChain/LangGraph, AWS Bedrock/Azure OpenAI, and production ML systems without either underselling myself or exaggerating?
What are some common interview mistakes that candidates don’t realize they’re making?
I’m not looking for generic advice like “keep applying,” “network more,” or “don’t give up.” I’m already doing that.
I’m specifically trying to understand what I need to change in my interview performance.
If you’re a recruiter, hiring manager, senior engineer, or someone who regularly interviews candidates, I would genuinely appreciate blunt and constructive feedback.
If you were in my position, what would you change first?
I’m taking my job search seriously and I’m completely open to criticism. I’d rather hear what I’m doing wrong than receive generic encouragement.
Thanks in advance.

Part 2 of my first post. TLDR on that post: AI sourcing is only as good as the data + search architecture underneath it.

The chat interface is the easy part.

The hard part is making sure the system has reliable and diverse data underneath it:

• Relying on one dataset limits you to that provider’s data coverage and, more importantly, how they structure and label profiles.
• If you only recruit for one type of profile... 1–2 providers may be enough. But the reality is most agencies (and independents) cover multiple industries, functions, and specializations. Especially if you're starting out, being nimble in industry coverage can help you win business as well.
• After hours of digging in, most (99%) providers aim to cover the same broad universe of LinkedIn profiles. No surprise.
• So the real difference is how well they label things like company type, seniority, industry, funding stage, skills, and career history.
• Each provider has different strengths across career history, company data, niche profiles, and freshness. Combining multiple datasets gives you better coverage, redundancy, and search quality.
• Example: “Senior Software Engineer in SF from a post-Series A startup.” Some providers can identify company stage and career context; others may only give you title + location.

Now, what does a good AI sourcing system look like... it's not as simple as just a "Claude Recruiting System" / DIY set-up you see being marketed that doesn't deliver high quality results.

To consistently surface the right results, a few layers need to work together.

• Intent understanding: Turn natural language, a JD, candidate, or company reference into the actual search criteria. Must-haves, preferences, job titles, experience and exclusions.
• Provider routing: Match that intent to the dataset best suited for the search and translate it into that provider’s schema.
• Enrichment: Waterfall across contact providers. Waterfall meaning if one provider doesn't find you a personal email, you can try another one.
• System Memory: Learn each recruiter’s niche and preferences so future searches get better with use. More on this below.

That’s where AI sourcing becomes more interesting.

Instead of generating a Boolean string, and feeling like you're starting a search from scratch every time, you’re providing context to an agent that can:

understand the search → choose the right data → execute the search → evaluate the results → learn from feedback → improve the next search.

And the same architecture shouldn’t stop at candidates.

The same platforms can search companies and jobs for client lead generation, identify decision makers, enrich their contact information, and monitor signals like hiring, funding, job changes, headcount growth, and new openings.

---

Spent a lot of time the last few months working through the hard parts above and finally at the point where this needs to be battle-tested.

No charge at all for accessing our databases or on contact info enrichment.

All we’re asking in return is that you bring real searches, push it on difficult mandates, and tell us where it falls short.

If that sounds interesting, send me a DM and I'll send you the invite!

Your hiring process is already an operations audit If a candidate passes three assessments, accepts the offer, and still has to ask Reddit who owns the next step, the problem is not “candidate experience.” It is a workflow with no visible owner. Delays happen. Silence without ownership is a system failure.

Currently in consulting, trying to switch into marketing, and the whole apply → find people → cold DM → follow up → repeat cycle is exhausting 😭

Has anyone here actually built a good AI workflow for this?

Thinking Claude/ChatGPT + extensions/agents/Clay/
etc. — anything that can help with:

finding the right roles/people
personalising outreach
tracking applications + follow-ups
figuring out what actually gets replies

Not looking to spam 500 applications. I want to make the process smarter + more efficient.

If you’ve tried something that genuinely worked, please share 🙏 Even if you can point me to a good post/video/person to learn from, that would be super helpful.

Is it faster decisions, better communication, structured interviews, skills-based evaluation, or something else?

Speaking as a moderator here.. wanted to share a perspective since this topic keeps coming up.

AI interviews have actually been a good addition, not something to fear. Consistent, judgement free, no reading a stranger's reaction in real time. Better scheduling, less waiting around, and you get to answer at your own pace instead of cramming into a rigid live slot.

They're also fair, same questions, same conditions for everyone, no interviewer having an off day.

Doesn't replace the human side of hiring, but as a first step, it has made things smoother for a lot of people, not harder.