Get hired atBizTrip AI×The Anti Job Board

How to Actually Get Hired

Who reads applications, which channel gets a reply, and what they screen for.

50+ peopleSan FranciscoAI

Don't make these mistakes

Don't send generic AI enthusiasm. Reference Yapta specifically - it was the first company to systematically monitor post-booking prices, and BizTrip is the AI-native evolution of that idea. If you can articulate why the current moment is different from 2007 and what that means for the product, you will have Romary's attention. For the TMC channel role, show you understand the travel industry from the inside.

What gets their attention

RRE leading the most recent tranche is the timing signal. RRE does not lead rounds in companies still finding product-market fit. They lead when the product works and the problem is distribution. Andrew Ng's AI Fund remaining the largest investor across three consecutive tranches means the ML architecture has passed internal review each time. Sabre Corporation is a strategic investor - they control booking infrastructure for thousands of enterprise travel programmes. If that relationship matures into a distribution agreement, it creates a customer pipeline. The gap between a working product and a team that sells it is precisely where you step in now.

Why applying the normal way doesn't work

BizTrip AI has an ATS but the real pipeline is referrals → sourced → recruiter picks → cold apps. You can still get through, but know where you stand.

Who to contact at BizTrip AI

Who decides:the hiring manager
Best channel:LinkedIn or direct email

What to show them

BizTrip's proprietary Travel LLM is the core technical asset. Building a domain-specific model that understands corporate travel policy language, GDS data formats, airline fare class rules, and hotel rate structures is a genuinely hard applied ML problem. The engineers who built the first version are the founding team. Scaling the model to handle more edge cases, more corporate policy types, and more GDS integrations requires adding engineering depth. Core skills: experience with LLM fine-tuning or domain-specific model training; familiarity with agentic AI architectures and multi-agent orchestration; ideally some exposure to travel data formats (GDS data, EDIFACT, TMC booking data); ability to work with real-time pricing signals and dynamic re-optimisation problems. For proof of work, build a minimal demonstration of what a 'travel agent' looks like: a simple Python script that takes a natural language travel request, identifies the key structured parameters, and outputs a set of hypothetical booking decisions with reasoning. Post it on GitHub. Add a README note on how you would approach the domain-specific fine-tuning challenge for corporate travel policy language.

A cold email that works at BizTrip AI

Subject: AI / ML Engineer (Travel Domain), [your one-line proof]
Tom, the Travel LLM problem is harder than a general-purpose travel assistant because corporate policy language is idiosyncratic and the fare class rules that determine price re-shopping eligibility are a domain most ML engineers have never encountered. I have been working on [domain-specific LLM / agentic AI] and I have a specific take on how you approach policy language encoding. Happy to share.

What BizTrip AI screens for

Their focus: RRE leading the most recent tranche is the timing signal. RRE does not lead rounds in companies still finding product-market fit. They lead when the product works and the problem is distribution. Andrew Ng's AI Fund remaining the largest investor across three consecutive tranches means the ML architecture has passed internal review each time. Sabre Corporation is a strategic investor - they control booking infrastructure for thousands of enterprise travel programmes. If that relationship matures into a distribution agreement, it creates a customer pipeline. The gap between a working product and a team that sells it is precisely where you step in now.

Don't send a generic CV to BizTrip AI. Mirror the job posting's language to get past automated screening.

Mistakes that kill applications

At people, a copy-paste CV is immediately obvious. It's an instant no.

Nobody cares what you want. Start with The corporate travel category is at the stage that expense management was in 2015, when Expensify and Brex were just beginning to attack Concur and how you'd help.

Don't apply and wait. The median AI application gets no response ever. One follow-up at day five roughly doubles reply rates.

Frequently asked questions

How do I apply to BizTrip AI?

Through the roles on our BizTrip AI jobs page, or directly to the hiring manager if you can reach them. At null people, direct outreach outperforms the form.

Does BizTrip AI respond to cold emails?

We're still collecting cold email data for BizTrip AI.

Who is the hiring manager at BizTrip AI?

At this size, hiring is usually run by the hiring manager.

How competitive is it to get hired at BizTrip AI?

-person AI companies see ~100-250 applicants per role in two weeks. The 72-hour window is your advantage.

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