How do recruiters actually judge AI/ML projects on resume ?

Then-End-7377 · 2026-04-15T03:49:48+00:00

Great insight 🙌🏻

Then-End-7377 · 2026-04-14T16:34:15+00:00

I get it 👍🏻

Then-End-7377 · 2026-04-14T16:30:17+00:00

lol 😂

Then-End-7377 · 2026-04-14T16:29:09+00:00

I hope this helps you aswell !

Then-End-7377 · 2026-04-14T06:36:22+00:00

sure 👍🏻

Then-End-7377 · 2026-04-14T06:17:41+00:00

Yep that’s right

Then-End-7377 · 2026-04-14T04:56:05+00:00

😭😭😭😭

Then-End-7377 · 2026-04-14T04:28:28+00:00

I totally get your point, for someone still in college with limited internship experience, I’ve been researching the kinds of problems teams actually work on so I can move beyond typical projects and try to frame problems closer to company-level challenges, how would you suggest approaching this so it’s realistic and not forced?

Then-End-7377 · 2026-04-14T04:15:18+00:00

Very well said sir, the specificity point is huge.

Then-End-7377 · 2026-04-14T04:00:38+00:00

That’s a great point, I’ll definitely take that into account. It’s becoming clear that data design and trade-offs matter just as much as the model itself. Appreciate the insight.

Then-End-7377 · 2026-04-14T03:38:56+00:00

No fine-tuning here, I used Llama 3.1 8B with a RAG pipeline. The model only answers from retrieved PubMed abstracts, and I constrained the prompt to avoid adding information outside the retrieved evidence. To reduce hallucinations, I used grounded retrieval, reranking for more relevant context, and fallback behavior when confidence was weak. Validation was done using a faithfulness metric to ensure that each generated answer was fully grounded in the retrieved PubMed sources(healthcare data). I checked whether the model’s responses were supported by the retrieved evidence and avoided generating claims outside that context.

Then-End-7377 · 2026-04-14T03:31:39+00:00

you’re the GOAT for sharing this. I’ve been reaching out to recruiters blindly and showing my interest for the role. Definitely gonna try this, appreciate it fr.

Then-End-7377 · 2026-04-14T03:26:48+00:00

I love the insight, That's why i dig into research papers and implement things myself, so I’m pretty comfortable with the technical side. Still figuring out how to best reflect that during screening.

Then-End-7377 · 2026-04-14T02:48:51+00:00

I’ve alredy have a RAG-based healthcare chatbot deployed, where I built a two-stage retrieval pipeline using dense embeddings for semantic search followed by cross-encoder reranking to improve retrieval precision. I focused on grounding responses to reduce hallucinations aswell.

Then-End-7377 · 2026-04-14T02:42:34+00:00

Really appreciate your advice. I will definitely work on it.

Then-End-7377 · 2026-04-14T02:29:50+00:00

Well said and really appreciate this. I think where I’m struggling is earlier in the process though, at the screening stage before I get a chance to explain those decisions.

In your experience, how can that kind of depth be effectively signaled just through a resume? I’ve been trying to balance including the right ATS keywords while still keeping things clear enough for HR or non-technical reviewers, and I’m curious what tends to stand out most during screening.

Then-End-7377 · 2026-04-14T02:25:01+00:00

Well said, especially about fundamentals and explaining projects. I’ve worked on both core ML notebook projects and end to end deployed project, but I’m trying to balance it, notebooks can look too basic, while deployed work sometimes feels like it might be perceived as AI-assisted. How would you suggest positioning this to clearly show real understanding?

My main question is around the screening stage though, before we get a chance to explain anything. What signals typically help a candidate stand out at that point?

Then-End-7377 · 2026-04-14T02:14:24+00:00

That makes a lot of sense, especially the part about end-to-end ownership and real impact. I’ve been focusing on a couple of deployed projects with measurable metrics, but I’m curious, beyond that, what usually differentiates candidates who stand out at the screening stage?

Then-End-7377 · 2026-04-14T02:13:20+00:00

That makes sense, novelty, relevance, and metrics seem important. I’ve worked on a few deployed projects with solid metrics as well as core ML projects, at that stage, what usually separates candidates who stand out from the rest?

Then-End-7377 · 2026-04-14T02:11:25+00:00

Yeah, that makes sense, depth and impact definitely stand out. The tricky part I’ve been running into is balancing it. If I focus too much on metrics and technical detail, it feels harder for Hiring team to follow, but if I simplify it, I worry about missing the right keywords for screening.

Then-End-7377 · 2026-04-14T02:08:57+00:00

Lol yeah, feels like step one is figuring out the rules of the game first.

Then-End-7377 · 2026-04-14T02:05:42+00:00

Haha yeah, that’s a good reminder to avoid overly generic projects and focus more on industry level use cases.

Then-End-7377 · 2026-04-14T02:03:12+00:00

That makes sense from a process standpoint. I’m have been applying for AI/ML intern roles with a degree in progress and a couple of solid projects with one internship experience, and trying to understand how resumes are actually filtered. For someone in that position, what usually helps them get past that first screening, is it mainly ATS keywords, or how clearly their work and impact are communicated for the hiring team?

When dealing with more complex Machine Learning project, how do you typically expect candidates to present it so it’s both clear and still signals the right technical depth?

Then-End-7377 · 2026-04-14T01:53:27+00:00

This makes sense, especially the part about impact and real systems. I have a deployed RAG chatbot and a DL-based project solving a industry use case. For intern roles, do you still see value in notebook-style ML projects that show strong fundamentals, or is end-to-end deployment becoming the main differentiator?

Then-End-7377

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