OpenJobs AI - End-to-End Autonomous AI Recruiter

@hello_leo Recruiting remains a significant challenge — one we're committed to tackling head-on, for both our team and our AI Agent.

@hello_leothanks leo for your comments !

@hello_leo we will do it better!

@luke_pioneero 
Thank you very much! We will continue to support startups and look forward to our startup program.

@luke_pioneero thanks luck for your comments!

@luke_pioneero Thanks for the good wishes！

@yuvraj_xyz This is fundamentally a complex noise-reduction problem. We tackle it by aggregating multi-dimensional data to improve the accuracy of information, all within appropriate and compliant boundaries of course.

Our goal is to surface better, more relevant candidates and bring them into the interview process. For candidates, building their own credibility over time matters just as much. We are continuously exploring and pushing forward. Thank you.

If you have any ideas or thoughts, we would love to hear them. Thank you so much.

@yuvraj_xyz The right question for 2026. Resume noise is no longer noise, it's the default.

Our take: stop treating the resume as ground truth. Mira triangulates against artifacts that are hard to fake, GitHub commits, papers, patents, OSS contributions, actual shipped products, and uses conversational screening to probe depth. Ask a real builder about a tradeoff they made and they go ten minutes. Ask an AI-optimized profile and it collapses in two turns. The asymmetry we're betting on: AI can write a resume, but it can't fake a track record of actually building things. The polish gets you in. The conversation gets you out.

@yuvraj_xyz thanks for your comments ! AI-optimized resumes are becoming the norm, so we don’t rely heavily on resume keywords alone.

At OpenJobs AI, we look much deeper into “builder signals” — things like actual project history, execution patterns, technical depth, consistency across experiences, public work, hiring context, and how candidates communicate/problem-solve over time.

A polished resume can help someone get discovered, but it’s very hard to fake real output, real momentum, and real domain understanding at scale. That’s where our agents focus.

@blink_66 You can describe your hiring needs just like you would in a conversation with a seasoned recruiter, and the right candidates end up booked directly on your calendar. This is not automation for the sake of automation. This is what the hiring process was always meant to be. It starts with what the Hiring Manager actually needs, and ends with the right candidate scheduled for an interview.
Thank you so much.

@blink_66 

Thanks! Funny enough, the most common reaction we get is "wait, was that actually an AI?", and not in the bad way. Candidates tell us it felt more specific and thoughtful than 90% of recruiter outreach they get. The design principle is simple: every message is generated per-candidate per-role, references real signals from their actual work, and the candidate can reply with real questions (comp, team, scope) and get real answers back. It's a conversation, not a blast. The bar we hold ourselves to: would a thoughtful senior recruiter on their best day send this? If not, Mira doesn't.

@blink_66 thanks for your question, so far, most of the human felt they are talking to a human recruiter.

@thea5 Feel free to try it with a real open role. You will find that candidates can be scheduled for interviews the same day. It is an incredibly efficient experience — moving straight to booking, confirming a time on the calendar. It really is that simple.
Thank you.

@thea5 the fastest result we recorded is half an hour

@thea5 generally speaking, we may receive about 10-15 candidates within 72 hours.

@dmitry_zakharov_ai many thanks!

@dmitry_zakharov_ai Thanks Dmitry!

@dmitry_zakharov_ai Thank you so much

@yehan_xiao 
Have an efficient conversation with the Agent，everything becomes simple and clear! A new way of recruiting has begun.

@yehan_xiao please give it a try! thanks for your comments

@yehan_xiao Founder hiring is brutal, you're hiring while doing everything else. Sourcing is the easy half. The follow-ups, the scheduling, the chasing, that's where founder time actually goes, and that's the part we built Mira to take. DM me when you're ready, I'll personally help you set up the first role.

@lokesh_motwani1 Thank you! We look at two separate problems: fit and intent.

For fit, Mira evaluates candidates beyond keyword matching — including trajectory, project relevance, skill overlap, and likely success in similar environments.

For intent, we use a mix of activity signals, responsiveness patterns, and inferred openness to opportunities, so recruiters spend less time reaching out to people who are unlikely to engage.

@lokesh_motwani1 Lokesh, thanks for trying it. You're naming the two problems that actually matter, and most "AI sourcing" tools mash them together. They're not the same.

Fit is: could this person do the job well if they joined? Intent is: would they actually take it if asked? A great candidate who isn't looking is a wasted ping. A motivated one who isn't a fit is worse, because nobody catches it until everyone's already burned hours on interviews. We score them separately.

For fit, we don't match on resume keywords. We reason about it. Which past projects actually demonstrate the role, which gaps are real, which ones a keyword filter would kill but a smart recruiter would push through.

@thekrew We think the personalization layer is the product. Mira doesn’t just blast recruiter-written templates at scale — it generates candidate-specific outreach based on profile, experience, signals, and role context, while still keeping recruiters in control of tone and constraints. The hard part is maintaining a quality floor as volume increases, so we optimize more for response quality and relevance than raw send volume.

@thekrew Sharp question, Vamshi. You're pointing at the exact failure mode that killed the first wave of "AI sequence" tools.

Our framing: the personalization-volume tradeoff is a symptom of treating outreach as a writing problem. It isn't. The real bottleneck isn't generating words per candidate. It's having something specific and true to say to each one that the recruiter couldn't have written themselves at scale.

Mira writes per-candidate, never from templates. But the part that actually matters happens upstream of the message. Before any outreach is generated, the Job Brief Agent and Search Agent produce a structured fit narrative for each candidate: which signals in their profile map to which requirements in the role, what's core versus adjacent, and what would plausibly motivate this specific person to take a call given their trajectory and current context. The message is just the surface rendering of that reasoning.

The reason quality doesn't collapse at volume is that we deliberately don't optimize volume. What we optimize for is qualified interviews, not messages sent or even reply rate. That changes the entire shape of the system. We'd rather contact 30 candidates with a 40% positive response rate than 300 with 4%. Unit economics are healthier, candidate experience stays dignified, and employers waste fewer interview slots.

The honest tradeoff we accept: Mira is slower per candidate than a template blaster. For technical and specialist hiring, which is where we're focused, that's the right side of the tradeoff to be on.

Would love to compare notes on how the sales-side version of this problem looks for you. The shapes seem to rhyme.