Get Freelancer Profile

Reads an AI trainer’s public profile for candidate evaluation — title, bio, skills, stats, label/work experience, education, languages, and reviews. Resolve candidates from proposals (candidate.id or candidate.profileSlug on GET /jobs/{id}/proposals), then invite or hire them.

Identity is masked: first name + last initial (agencies show the agency name with no last-name initial). Personal contact details — including personal email — are never returned; see privacy. Profiles you cannot view (nonexistent, closed, banned, or restricted by the AI trainer’s visibility settings) return 404, not 403.

Requirements: proposals:read scope. Works pre-claim.

Request

idOrSlugstringpathrequired

The AI trainer’s user ID or public profile slug.

Response

freelancerobject

freelancer

idstring

User ID.

profileSlugstring | null

Public profile slug.

profilePathstring

App-relative profile path (append to https://app.opentrain.ai).

displayNamestring

Masked display name, e.g. Maria G..

firstNamestring | null

First name.

lastNameInitialstring | null

Last-name initial. null for agencies.

talentTypestring | null

Individual or Agency.

titlestring | null

Profile headline.

biostring | null

Profile description.

citystring | null

City.

countryCodestring | null

ISO country code.

countrystring | null

Country display name.

profilePhotoUrlstring | null

Profile photo URL.

hourlyRateUsdnumber | null

Listed hourly rate.

availabilitystring | null

Stated availability.

expertiseLevelstring | null

Self-reported expertise level.

englishLevelstring | null

English proficiency.

memberSincestring

ISO account-creation timestamp.

identityVerifiedboolean

true when identity verification is complete.

skillsobject

{subjectMatter[], tags[], software[], dataTypes[], labelTypes[]} — string arrays.

languagesobject[]

{language, proficiency} pairs (proficiency may be null).

statsobject

{totalEarnedUsd, billedHours, jobSuccessScore, reviewCount, averageRating} — averageRating is rounded to one decimal, null when unrated.

agencyobject | null

For agencies: {name, website, headcount, industryExperience[]}. null for individuals. website is the agency’s public business website and is returned as stored.

labelExperienceobject[]

Labeling-specific experience entries: {id, title, description, fromYear, toYear, ongoing, software, dataType, labelTypes[]}.

workExperienceobject[]

General work history: {id, title, company, description, fromYear, toYear, current, city, country}.

educationobject[]

{id, school, degree, areaOfStudy, fromYear, toYear, description}.

reviewsobject[]

Employer reviews: {id, rating, comment, createdAt} — rating rounded to one decimal, createdAt ISO. Contact data in comment is replaced with a placeholder: emails with [REDACTED_EMAIL], phone numbers with [REDACTED_PHONE], postal addresses with [REDACTED_PHYSICAL_ADDRESS], messaging handles with [REDACTED_CONTACT_HANDLE], and links with [REDACTED_LINK]. The links removed include URLs, www addresses, hosts with a path or a port, IPv4 addresses with a path, domain names under country-code or common top-level domains, and some spelled-out domains, such as acme dot com. A domain with no path under a rare top-level domain, such as .photography, can stay. A comment with no contact data comes back unchanged. The stored review does not change.

redactionsobject[]

One entry for each kind of contact data removed from one text field: {field, kind, replacement, count, source}. field is the path, such as bio or reviews[0].comment. kind is EMAIL, PHONE, PHYSICAL_ADDRESS, CONTACT_HANDLE or LINK, and replacement is its placeholder. source is CANDIDATE_PROFILE for text the AI trainer wrote (title, bio, city, skills, languages, agency text, and experience and education text), which loses emails, phone numbers, postal addresses and messaging handles. source is EMPLOYER_REVIEW for a review comment, and only those entries can have kind LINK. An entry never contains the removed text. A field with no contact data has no entry, so a clean profile returns [].

Errors

Status	code	Meaning
400	BAD_REQUEST	Missing idOrSlug
401	UNAUTHORIZED	Missing or invalid token
403	FORBIDDEN	Missing proposals:read scope
404	NOT_FOUND	No such profile, or the profile is not visible to you (details: {resource: "freelancers", idOrSlug})

Request examples

curlCLIMCP: opentrain_get_freelancer_profile

curl

curl -sS https://app.opentrain.ai/api/public/v1/freelancers/<USER_ID_OR_SLUG> \
  -H "Authorization: Bearer $OT_API_TOKEN"

CLI

opentrain freelancers get --id <USER_ID_OR_SLUG> --json

MCP: opentrain_get_freelancer_profile

{
  "idOrSlug": "<USER_ID_OR_SLUG>"
}

Response examples

200

{
  "freelancer": {
    "id": "<FREELANCER_ID>",
    "profileSlug": "maria-g",
    "profilePath": "/profile/maria-g",
    "displayName": "Maria G.",
    "firstName": "Maria",
    "lastNameInitial": "G",
    "talentType": "Individual",
    "title": "Text Annotation Specialist",
    "bio": "Native Spanish speaker with 4 years of NLP annotation experience.",
    "city": "Madrid",
    "countryCode": "ES",
    "country": "Spain",
    "profilePhotoUrl": "https://app.opentrain.ai/<PHOTO_PATH>",
    "hourlyRateUsd": 18,
    "availability": "FULL_TIME",
    "expertiseLevel": "EXPERT",
    "englishLevel": "FLUENT",
    "memberSince": "2024-03-02T10:00:00.000Z",
    "identityVerified": true,
    "skills": {
      "subjectMatter": ["Social media", "Customer support"],
      "tags": ["SENTIMENT_ANALYSIS", "TEXT_CLASSIFICATION"],
      "software": ["<ANNOTATION_TOOL>"],
      "dataTypes": ["TEXT"],
      "labelTypes": ["CLASSIFICATION"]
    },
    "languages": [
      { "language": "Spanish", "proficiency": "NATIVE" },
      { "language": "English", "proficiency": "FLUENT" }
    ],
    "stats": {
      "totalEarnedUsd": 2400,
      "billedHours": 310,
      "jobSuccessScore": 98,
      "reviewCount": 7,
      "averageRating": 4.9
    },
    "agency": null,
    "labelExperience": [
      {
        "id": "<EXPERIENCE_ID>",
        "title": "Spanish tweet sentiment corpus",
        "description": "Annotated 120k tweets for a sentiment model.",
        "fromYear": 2023,
        "toYear": 2025,
        "ongoing": false,
        "software": "<ANNOTATION_TOOL>",
        "dataType": "TEXT",
        "labelTypes": ["CLASSIFICATION"]
      }
    ],
    "workExperience": [],
    "education": [],
    "reviews": [
      {
        "id": "<REVIEW_ID>",
        "rating": 5,
        "comment": "Fast, precise, great guideline questions.",
        "createdAt": "2026-01-20T12:00:00.000Z"
      }
    ],
    "redactions": []
  }
}