DataAnnotation AI training jobs

The newest models can vibe-code a whole feature in minutes. Someone has to check whether that code actually works, and that someone is you.

Most of what models get wrong isn't specialist knowledge. It's careful reading, clear writing, and common-sense judgment, and that's exactly what this role supplies.

The newest models can spin up a full analysis in minutes — load the data, pick a method, produce charts and a confident conclusion.

Models can build a three-statement model that looks clean and still get the mechanics wrong: a balance sheet that doesn't balance, working capital that flows the wrong way, a circular…

Ask a model a tax question and it answers with authority, then applies a code section that changed last year, misses a phaseout, treats an S corp like a partnership, and forgets that state…

Ask a model to close the books and it produces entries that foot perfectly and still misstate the result: a lease booked the old way, revenue recognized a quarter early, an intercompany…

Models produce fluent legal analysis that is often subtly wrong: invented citations, misapplied precedent, and jurisdictional confusion.

Models produce fluent legal analysis that is often subtly wrong: invented citations, misapplied precedent, and jurisdictional confusion.

A model will draft a cite-check that looks airtight and still miss that the case was overruled, point to the wrong treatment, and quietly invent a pin cite.

Models answer health questions in a confident, authoritative voice and still get the clinical details wrong: a contraindication they skip, guidance that ignores comorbidities, advice that…

We’re looking for an experienced Radiologist to help train AI models.

Medical questions are where model mistakes matter most: outdated guidance, missed contraindications, and confident answers that no clinician would sign off on.

Models sound confident and still get bedside care wrong: a dose no nurse would give, a triage order that buries the sickest patient, discharge teaching that misses the one thing keeping…

Models handle textbook finance fine and fall apart on the real thing: mispriced risk, subtly wrong derivations, and models that ignore market microstructure.

Models can recommend a portfolio that reads well and quietly breaks every rule that matters: concentrated bets dressed as diversification, a Sharpe ratio computed on overlapping exposures,…

Models struggle with the practical side of healthcare: billing codes, insurance workflows, scheduling, and the administrative details that keep clinics running.

Models will hand you code that looks secure and quietly ships a hardcoded secret, a broken auth check, or an injection the linter never flags.

Models ship UI that renders in the demo and breaks everywhere else: state that desyncs on the second render, an accessibility tree no screen reader can follow, a layout that collapses the…

A model will hand you a pipeline and an infrastructure change that apply cleanly and quietly leave a security group open to the world, a state file that will corrupt on the next run, and a…

Models can generate a slick-looking screen and completely miss the point: the flow that adds three needless steps, the hierarchy that buries the one action that matters, the contrast that…

Models write backend code that passes the happy-path test and falls over on everything else: the race condition under load, the N+1 that melts the database, the API contract that breaks…

Models are surprisingly bad at reasoning about themselves: training dynamics, evaluation design, data pipelines, and deployment trade-offs.

Models routinely propose impossible mechanisms, unsafe procedures, and syntheses that no bench chemist would attempt.

Models can recite physics and still get it wrong: dimensional errors, misapplied approximations, and derivations that skip the step where the physics actually happens.

A model will answer a compliance question with confidence and still point you toward a HIPAA violation: a disclosure with no authorization, a retention rule from the wrong state, a Stark or…

Japanese exposes AI quickly: it mishandles keigo, mixing polite, humble, and honorific speech in ways that would embarrass a real speaker, and it fumbles counters and particles.

AI can string together grammatical Mandarin and still read as foreign: it fumbles measure words, reaches for stiff textbook phrasing, and drops idioms like chengyu at the wrong pitch.

Thai gives AI little room: words run together without spaces, tone carries meaning, and the politeness particles shift with who is speaking.

Vietnamese is unforgiving of the small errors AI makes constantly: it drops or misplaces the tone diacritics that change a word entirely, and it stumbles over the pronoun system, where the…

Korean punishes the mistakes AI loves to make: it mixes speech levels, sliding between formal and casual within a single exchange, and drops or misplaces the particles that carry meaning.

Frontier models still fail at rigorous multi-step mathematics: invalid derivations, unjustified leaps, and confidently wrong final answers. Automated checks can't grade a proof. You can.

Models produce confident accounting answers that fail on the details: misapplied standards, broken reconciliations, and journal entries that don't balance.

Automated metrics can't judge whether Spanish text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.

Models can scaffold a whole app in one shot: front end, API, and database, all of it looking right. Then the state goes stale, the endpoint leaks data, and the migration bricks production.

Models blur the line between what's established, what's contested, and what's simply made up in the life sciences, and they cite papers that don't exist.

We’re looking for experienced bench scientists to help train AI models.

We’re looking for experienced drug discovery scientists to help train AI models.

We’re looking for experienced computational biologists and bioinformaticians to help train AI models.

We’re looking for an experienced Mechanical Engineer to help train AI models.

We’re looking for an experienced Mechanical Design Engineer to help train AI models.

We’re looking for an experienced Validation Engineer to help train AI models.

We’re looking for an experienced Automation Technician to help train AI models.

We’re looking for an experienced Mechanical Designer to help train AI models.

We’re looking for an experienced Senior Mechanical Design Engineer to help train AI models.

DataAnnotation is looking for an experienced Electrical Engineer to evaluate how frontier AI models handle real electrical and electronics engineering work.

DataAnnotation is looking for an experienced Embedded Systems Developer to evaluate how frontier AI models handle real electrical and electronics engineering work.

DataAnnotation is looking for an experienced Hardware Engineer to evaluate how frontier AI models handle real electrical and electronics engineering work.

DataAnnotation is looking for an experienced Electronics Engineer to evaluate how frontier AI models handle real electrical and electronics engineering work.

Models assign codes that look right and bill wrong: an ICD-10-CM code one notch short on specificity, a CPT modifier that changes the whole claim, an NCCI edit ignored, an E&M level the…

Models can describe an exploit chain in confident detail and miss that step three never actually works, the payload gets filtered, or the whole path assumes a misconfiguration no real…

Automated metrics can't judge whether Hindi text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.

Ask a model to brief a motion and it hands back something that reads like a real filing, then cites three cases that do not exist and misstates the standard of review on a fourth.

Ask a model to plan an audit and it produces a tidy program, then sets materiality by feel, accepts a management assertion as if it were evidence, and designs a sample that would never…

Models describe the revenue cycle in clean theory and miss the mechanics that decide whether a claim pays: the real reason behind a CARC or RARC denial code, a payer rule that overrides the…

During an incident, models love a tidy story: one root cause, a clean timeline, a confident containment plan.

Automated metrics can't judge whether Portuguese text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.

Models write a pitch deck's worth of confident deal analysis and miss what a deal team lives on: a comps set stuffed with the wrong peers, an accretion/dilution model that ignores the…

Hand a model a merger agreement and it summarizes it fluently, then misses that the indemnification cap swallows the escrow, that a closing condition has no matching covenant, and that the…

Models sound reassuring and still get bedside care wrong: a dosing detail that no nurse would administer, a triage order that buries the sickest patient, discharge teaching that misses the…

A model will design an audit approach that reads well and would never survive review: a sample that tests the wrong assertion, a control it calls effective without evidence, a materiality…

Models are great at spotting the textbook SQL injection and blind to the flaw that only shows up in your architecture: the trust boundary drawn in the wrong place, the deserialization no…

Automated metrics can't judge whether Arabic text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.

Models build an LBO that returns a tidy IRR and skip the parts that make or break a deal: a debt schedule that ignores the covenants, an entry multiple with no exit story, diligence that…

Ask a model about a regulatory obligation and it answers with confidence, then cites a rule that was superseded two years ago, blurs the line between binding regulation and informal…

Models recite cardiology confidently and still miss what matters: an outdated guideline threshold, a misread rhythm strip, an anticoagulation choice that ignores a clear contraindication.

Ask a model to build a forecast and it returns a clean-looking model whose drivers do not connect, whose assumptions defy the business, and whose variance bridge does not tie to actuals.

Models explain managed care in tidy theory and miss the mechanics that decide coverage: a benefit rule that overrides the general one, a prior-auth requirement waived in the contract, a…

Models approve a borrower that any underwriter would flag: cash-flow coverage computed off the wrong line, covenants that don't bite, a rating that ignores where we are in the cycle.

Models produce documentation that reads well and fails on integrity: a note that does not support the code billed, a missed query opportunity, a present-on-admission indicator that shifts…

Automated metrics can't judge whether Indonesian text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.

Models give financial advice that sounds reassuring and quietly ignores the client: a tax-blind withdrawal plan, an allocation that forgets the time horizon, an estate suggestion that would…

Give a model a contract to redline and it produces clean-looking edits that quietly leave an obligation with no trigger, a defined term used two different ways, and a cross-reference…

Hand a model a revenue arrangement and it writes a confident memo, then misapplies the ASC 606 steps, parks a lease in the wrong place under ASC 842, drops a required disclosure, and…

Automated metrics can't judge whether Turkish text actually sounds right: tone, idiom, cultural nuance, and the thousand small choices a native speaker makes without thinking.

Models write an equity note that sounds like the sell side and falls apart under a covering analyst's read: an estimate that ignores guidance, a target price with no bridge from the model,…

Ask a model to assess patentability and it reasons smoothly, then treats an abstract idea as eligible subject matter, waves past the closest prior art, and construes a claim in a way no…

AI writes fluent-looking French that still slips on what natives feel automatically: it mishandles tu and vous, forces gender agreement in odd places, and reaches for wording that sounds…

Models reach for an actuarial answer and fumble the long-tail math: a reserve that ignores development, mortality assumptions applied to the wrong cohort, discounting that quietly breaks…

German gives AI plenty to get wrong: it botches case endings, builds compound nouns no one would actually say, and misses when du should be Sie.

Models love a founder's story and can't tell a real market from a rounded-up one: a TAM built by multiplying optimism, traction metrics that don't cohort, a moat that's really just a…

Models bind a risk no underwriter would touch: a premium that ignores the loss history, exclusions that don't match the exposure, a claim decision that misreads the policy language.

AI produces textbook Italian that still feels off: it forces gender and number agreement mechanically, misses the formal Lei, and leans on wording that sounds translated rather than said.

Models talk about markets like a textbook and trade like they've never been filled: a strategy that ignores the bid-ask, a hedge that's wrong by a Greek, position sizing with no respect for…

Models manage cash on paper and miss what keeps a company solvent: a liquidity forecast that ignores timing, an FX hedge that leaves the real exposure open, a funding plan that assumes the…

We’re looking for an experienced Management Consultant to help train AI models.

Russian trips AI on structure it cannot fake: six cases, verb aspect, and agreement that has to hold across a whole sentence.

Tagalog trips AI on the verb focus system and on the po and opo markers that carry respect.

Bengali catches AI on the you it chooses: tui, tumi, and apni each carry a different closeness, and the model picks wrong.

Polish gives AI a lot to break: seven cases, verb aspect, and agreement that must stay consistent across the sentence.

Tamil catches AI on its split between everyday spoken forms and formal literary Tamil: the model mixes the two, producing text no one would say aloud or write in earnest.

Hebrew exposes AI through grammatical gender: verbs and adjectives agree with the person addressed, and the model routinely gets it wrong.

Malay trips AI on register: formal written Malay and everyday colloquial speech pull in different directions, and the model blends them awkwardly.

Dutch catches AI on word order and on the je versus u choice that sets the tone of a sentence.

Evaluate AI on Croatian translation, localization, and cultural nuance across real projects.

Evaluate AI on Icelandic translation, localization, and cultural nuance across real projects.

Evaluate AI on Slovak translation, localization, and cultural nuance across real projects.

Ukrainian gives AI structure it cannot fake: cases, verb aspect, and agreement that must hold across the sentence, plus the ty and vy distinction.

Evaluate AI on Catalan translation, localization, and cultural nuance across real projects.

Evaluate AI on Czech translation, localization, and cultural nuance across real projects.

Evaluate AI on Danish translation, localization, and cultural nuance across real projects.

Evaluate AI on Finnish translation, localization, and cultural nuance across real projects.

Evaluate AI on Gujarati translation, localization, and cultural nuance across real projects.

Evaluate AI on Kannada translation, localization, and cultural nuance across real projects.

Evaluate AI on Malayalam translation, localization, and cultural nuance across real projects.

Evaluate AI on Marathi translation, localization, and cultural nuance across real projects.

Evaluate AI on Norwegian translation, localization, and cultural nuance across real projects.

Evaluate AI on Odia translation, localization, and cultural nuance across real projects.

Evaluate AI on Punjabi translation, localization, and cultural nuance across real projects.

Evaluate AI on Romanian translation, localization, and cultural nuance across real projects.

Evaluate AI on Swedish translation, localization, and cultural nuance across real projects.

Evaluate AI on Telugu translation, localization, and cultural nuance across real projects.

Evaluate AI on Urdu translation, localization, and cultural nuance across real projects.