Dishaya / Careers / AI Research Engineer

AI Research Engineer

Bridge open research questions and shipped capability: what should the product be able to verify next, and how do we know it works.

Engineering · Remote-First · Founding Equity

About Dishaya

Dishaya makes research people can actually send. One question in; a client-ready report, deck, and source ledger out, with every claim checked against the exact passage it cites and labeled verified, partial, unverified, or contradicted. What fails the check is disclosed, never hidden. We are early, independent, honest about both, and building toward one ten-year outcome: "Verified by Dishaya" becoming a mark a reader trusts before they read. More in About and Principles.

Why This Role Matters

The underlying technology gets better every few months. The open question is what Dishaya can honestly verify next, and answering it is a job, not a side effect. This role owns that job: turning "could we?" into a prototype, a prototype into evidence, and evidence into a capability users can trust on day one. Without this role we ship what is easy; with it we ship what is next.

What You'll Work On

• What the product can verify next: prototyping new capabilities that extend what a verified answer can cover, and carrying the winners to production.
• Honest experiments: designing tests that are allowed to fail, so a capability ships only when we know how well it works and where it does not.
• Staying ahead of better base models: when the technology improves, translating that into user-visible quality quickly rather than eventually.
• Published limits: every capability you ship arrives with a plain statement of what it cannot do yet.

We describe work by the outcomes you will own rather than by our internal systems; you will see everything on the inside from day one.

Responsibilities

• Prototype verification-adjacent capabilities, run them against real user questions, and make a clear ship-or-shelve call.
• Carry winning prototypes into production quality and own them there.
• Design experiments before you build the feature; report the results whether or not they flatter the idea.
• Track what improving base models make newly possible and turn that into a concrete, prioritized plan.
• Write things down: experiment designs, findings, limits, and the reasons behind every ship-or-shelve decision.

Required Qualifications

• You have shipped product features built on large language models and can explain what worked, what did not, and how you knew the difference.
• Experimental rigor: you design the test before the feature, you distrust your own demos, and you can tell a real result from a lucky one.
• You move comfortably between prototype code and production code, and you are honest about which is which.
• Clear writing: your experiment write-ups can be read by someone who was not there and acted on.

Preferred Qualifications

• Experience with retrieval, grounding, or citation-backed generation in a product users paid for.
• You have shelved your own project on evidence and can talk about it without flinching.
• A background where claims had to survive checking: applied research, measurement-heavy engineering, or a research-adjacent product.

Nice To Have

• Published writing, talks, or open-source work that shows how you think.
• Experience turning research-style results into features real users kept using.

What Success Looks Like

• 30 days: you have reproduced our current quality picture yourself, questioned it in writing, and run your first small experiment end to end.
• 90 days: your first prototyped capability has a verdict: shipped to users with its limits published, or shelved with a written reason. Either outcome counts if the evidence is honest.
• 365 days: at least two capabilities you prototyped have reached users, each with published honest limits, and the team's answer to "what can we verify next" is a tested plan rather than a guess.

Team Principles

• Honesty over fluency, in the product and in code review.
• Delete before you add; every abstraction earns its keep.
• Evidence over enthusiasm; direction comes from users.
• Small, senior, trusted; you own outcomes, not tickets.

Benefits

• Founding-level equity; early means it matters.
• Remote-first, judgment over time zones.
• The hardware and tools you need, without a procurement dance.
• Direct access to how the company runs: numbers, decisions, reasons.

Interview Process

1. Intro conversation (30 minutes): the honest state of the company, and what you want to build.
2. Craft deep-dive: real decisions inside work you shipped.
3. Paid working session: scoped, close to the real job, never spec work we ship.
4. References and a clear written offer, fast.

Equal Opportunity

Dishaya is an equal opportunity employer. We evaluate candidates on craft, judgment, and alignment with how we work, never on race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or veteran status.