OpenJobs AI is an end-to-end autonomous AI recruitment platform, powered by an AI agent named Mira at its core, capable of automating the entire recruitment process around the clock — from candidate identification and outreach to preliminary screening.
This is the 2nd launch from OpenJobs AI. View more
OpenJobs AI
Launched this week
Tell us what role you’re hiring for. AI recruiter sources qualified candidates, screens them against your requirements, sends personalized outreach, tracks replies, and books interviews directly on your calendar.
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Launch Team / Built With
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Recruiting is one of the loudest categories on AI search right now — I run a tool that watches MentionFox's visibility across ChatGPT/Claude/Gemini/Perplexity and "AI recruiter" comes up dozens of times a week with the same 4-5 names cited (almost all enterprise tools you have to demo to even price). Curious — what's your current strategy for showing up in those AI-assistant answers? That's where job-seekers and HR folks search now, not just Google. We see a real gap between which tools are recommended in chatbots vs. which tools actually solve the problem at SMB price points. Congrats on the launch. The autonomous angle is the right wedge — manual recruiter SaaS is exhausting at scale
The "candidate identification and outreach" piece is what we pay the most attention to building in the B2B sales automation space — and it's genuinely the hardest part to get right at scale. Curious how Mira handles the personalization-to-volume tradeoff: does it generate outreach messages per candidate, or does it work from templates the recruiter configures? The quality floor on AI outreach tends to collapse fast when volume goes up.
OpenJobs AI
@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.
OpenJobs AI
@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.
Agnes AI
How does Mira handle niche roles like quant or biotech? Or is it mostly noise?
OpenJobs AI
@cruise_chenNiche is exactly where Mira shines. Keyword sourcing breaks on quant and biotech because the signal is in the sub-specialty, not the title. Mira reads JDs at that resolution: stat arb vs market making, wet-lab vs computational bio, mRNA delivery vs protein engineering. That's why AI, robotics, and biotech became our biggest verticals, the deeper the domain, the worse traditional tools perform, and the bigger our edge. Quant's the same architecture. Give Mira a real JD and you'll see a slate same-day from a pool 300x larger than manual sourcing. Try it on your hardest open role.
OpenJobs AI
@cruise_chen thanks for the comments, please see my CTO's feedback.
OpenJobs AI
@cruise_chen
To add some context — the objective scarcity of qualified candidates is a genuinely difficult challenge, and one that no algorithm alone can solve. It is fundamentally a supply problem. That said, when each role is communicated with greater clarity and specificity, it meaningfully improves the likelihood of a successful match.
What traditional recommendation engines and search engines have failed to deliver, we have built: a results-driven, interactive talent-sourcing Agent. When you experience it firsthand, I am confident it will exceed your expectations. Thank you.
A little worried about candidate experience. Aggressive automated outreach is already burning out top candidates. How does OpenJobs AI avoid contributing to the noise?
OpenJobs AI
@jinhao_bai2 Spamming candidates is simply the wrong approach. We only surface roles that are accurate and genuinely relevant to each individual. Modeling candidate intent is a complex engineering challenge, and one we are continuously iterating on and exploring.
When information is truly precise and scarce, it carries real weight for the candidate. That is why our response rate consistently outperforms the industry average. We will keep optimizing and delivering more value.
OpenJobs AI
@jinhao_bai2 This is exactly the question that should be asked, and honestly, it's why we exist.
The reason candidate experience is broken isn't AI, it's that recruiters were forced to use templates and bulk tools to keep up with volume. AI done wrong amplifies that. AI done right inverts it. Mira sends fewer messages, not more. Every outreach is gated by a qualification step, generated per-candidate per-role, and rate-limited across our entire network so no one gets hit twice. Candidates can actually have a conversation back, asking about comp, team, scope, before deciding to engage. The candidate experience metric is in our north star, not a footnote. If Mira contributes to the noise, our whole thesis collapses.
OpenJobs AI
@jinhao_bai2 That’s a real concern, and honestly we think the industry is heading in the wrong direction if AI just means “more spam at scale.”
Our approach is to optimize for relevance, timing, and signal quality — not outreach volume. The goal is fewer but much higher-context interactions, where the agent already understands why this opportunity actually matches the candidate.
Long term, we believe recruiting agents should behave more like trusted talent scouts, not mass outbound tools.
Tried it on a niche role where I was genuinely skeptical the AI would find anyone good. It surfaced candidates I hadn't found in 3 weeks of manual search. Real respect！Curious — how are you guys approaching growth and distribution right now? Feels like this could scale really well through SEO/GEO around hiring workflows, job titles, and recruiter intent searches.
OpenJobs AI
@whitney_hong Appreciate it — that’s exactly the kind of workflow we’re trying to unlock.
Right now our focus is less on paid growth and more on building deep product loops inside recruiting workflows. SEO/GEO around recruiter intent, niche roles, and hiring outcomes is definitely a big part of the strategy, especially as the agent gets better from real usage data. We think distribution in recruiting will increasingly come from performance, not just traffic.
OpenJobs AI
@whitney_hong Thank you for your feedback and guidance on our direction.
The multi-agent collaborative model, built around outcomes and data, changes everything compared to traditional approaches. It brings us closer to the nuanced judgment of a seasoned recruiter, while being fairer and more efficient at the same time.
We believe methodology and approach are everything. We also believe great products speak for themselves through word of mouth. So our focus stays on continuously raising the bar on our service and product, and delivering real, tangible results for our clients and users.
We welcome more people to come and try it out. The more feedback we receive, the faster our Agent and our team can improve. Thank you.
OpenJobs AI
@whitney_hong Three weeks of manual search beaten by an agent is exactly the wedge. Thank you for stress-testing it on a hard role, that's the signal we actually care about. On distribution, you're half right. SEO/GEO around recruiter intent is the obvious play, and yes, we're running it. But the bigger bet is agent-to-agent: Mira is exposed via API and MCP, so she shows up inside Claude, GPT, and any internal copilot a recruiter already uses. "Hiring workflow" stops being a destination, becomes a capability. Which role broke your manual search? Curious. 👀
Congratulations on the launch. Tried it. How do you find if the candidate is really fit for the job and is actually looking for the job?
OpenJobs AI
@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.
OpenJobs AI
@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.
What's the user experience like as a candidate that Mira reaches out to once a call has been booked? Is the call with an AI agent or with an actual human recruiter from the hiring company?
OpenJobs AI
@ebroms Once a candidate books a call, the experience can be fully configurable by the employer. Some companies prefer an AI-led screening interview first for speed and consistency, while others route candidates directly to a human recruiter or hiring manager. Our goal is to reduce recruiter overhead without making the candidate experience feel robotic or impersonal.
OpenJobs AI
@ebroms When someone books a call with us, they're talking to a real human at the company. A recruiter or the hiring manager, depending on the role. Mira doesn't sit in for the interview.
That's intentional. The moment a candidate agrees to a call, they're making a real career decision, and finding an AI on the other end would feel like getting catfished. People are fine chatting with an AI to confirm the boring stuff. Timing, scope, comp range, whether the role's actually remote. They're not fine pitching their career to a chatbot for 30 minutes, and frankly, they shouldn't have to.
Our split is pretty simple. AI does what AI is actually good at: searching, personalizing, juggling async threads, getting calendars to agree with each other. Humans do what humans are still way better at: reading whether someone's the right fit, conveying what the team's actually like, closing. Candidates get both. Nobody has to pretend to be the other one.