Upwork Job Scraper – Freelance Job Listings, 58+ Filters

New Upwork postings, usually within 10 minutes of going live. A loop watches the board continuously, so this Actor answers from an index that is already current instead of going to Upwork when you ask. Calls return in seconds, and 58 filters with range and comparison operators cut the firehose down to the jobs worth bidding on. $2 per 1,000 results — no monthly fee.

Built for freelancers, agencies, and job-board builders who need to see a posting before everyone else has replied to it. Most scrapers go to Upwork at request time and hand you raw rows. This one is already watching.

Why this one, not the other Upwork scrapers

	This scraper	Typical Upwork scraper
Freshness	✅ Measured: 98.7% of postings available within 10 minutes of going live, median under 4 minutes	⚠️ Whatever the page happens to show at request time
Speed	✅ Served from a maintained index — seconds per call, with no target site to rate-limit or block you	❌ Live scrape on every run
Filter depth	✅ 58 fields, with range and comparison operators — ">=500", "500-2000" — across budget, client hire rate, total spent, payment-verified, avg rate and feedback	⚠️ ~5–15 basic filters
Real-time "new jobs only"	✅ Returns only jobs new since your last call, via an automatic per-client cursor — built for polling on a schedule	❌ Returns everything, every time
AI enrichment	✅ Skills, summaries, urgency & client tech-savviness extracted per job	❌ Raw text only
Screening questions	✅ Captured per job — pipe straight into your proposal generator	❌ Hidden until you open the posting
Price	✅ $2 / 1,000 results — pay per result, no monthly fee	⚠️ $10–25/mo flat fees

No monthly fee, no commitment. You pay only for the results you keep, and zero-result runs cost nothing — which is the point when you poll: the quiet checks between postings are free, so a tight schedule costs no more than a lazy one.

📈 Who it's for

• Freelancers — poll every few minutes and see matching jobs while they are still fresh, with screening questions ready to drop into your proposal.
• Agencies — filter straight to high-budget, agency-friendly clients with real spend and hire history.
• Job boards & SaaS — power a niche Upwork job board or alerting product on a feed that stays current without you running scrapers.
• Market researchers — track pricing, demand, and skill trends across millions of postings.

🚀 Quick Start

1. Basic Usage

{
  "limit": 10
}

2. With Smart Filtering

{
  "limit": 20,
  "skills": "React",
  "price_min": ">=50",
  "buyer_score": ">=4.0",
  "ai_urgency": "Urgent"
}

3. Notification Mode (Only New Jobs)

{
  "notifications_only": "true",
  "limit": 50
}

📤 Output Format

Each dataset item is a flat job record. Real example from a fresh posting (job_score: 94):

{
  "id": "022053503680858460814",
  "title": "Senior React Native / Cloud Backend Engineer for iOS Video App",
  "url": "https://www.upwork.com/jobs/~022053503680858460814",
  "description": "We are looking for a senior engineer to join a small, fast-moving team building a live iOS app in React Native with heavy cloud backend connectivity and cloud-based video processing. This is not a web app project. We need someone experienced with real iOS apps on real devices, App Store/TestFlight workflows, mobile-to-cloud video uploads, client/server sync, and production-safe feature deployment...",
  "date_posted": "2026-05-10T15:53:24Z",
  "created_at": "2026-05-10T15:59:01Z",
  "price_type": "Hourly",
  "price_min": 70,
  "price_max": 100,
  "skills": "React Native, iOS, Mobile App Development, Google Cloud Platform, Python, AI Development",
  "category_name": "Web, Mobile & Software Dev",
  "subcategory_name": "Web Development",
  "experience_level": "Expert",
  "engagement_label": "3 to 6 months",
  "qual_pref_english": "ANY",
  "client_location": "United States",
  "buyer_city": "Miami",
  "buyer_score": 4.93,
  "buyer_feedback_count": 44,
  "buyer_payment_verified": true,
  "jobs_posted": 57,
  "hires": 55,
  "hire_rate": 100,
  "total_spent": 54226.41,
  "avg_hourly_rate": 55.35,
  "total_hours": 473,
  "open_jobs": 1,
  "ai_clients_technical_understanding": "High",
  "ai_technical_skills": "React Native, iOS app development, App Store/TestFlight workflows, mobile video/photo upload, camera roll/photo library permissions, cloud backend (GCP preferred, AWS acceptable), async jobs/queues/workers, client/server state sync, production logging/debugging, AI-assisted development",
  "ai_inferred_technical_skills": "Slack, Markdown documentation, real device testing",
  "ai_explicit_mention_of_agency": "No Mention",
  "ai_summary": "You'll build and maintain a production iOS app in React Native focused on cloud-connected video/photo uploads, delivering fully tested features on real devices with App Store/TestFlight workflows. The client expects a senior engineer with deep mobile iOS experience (real-device testing with a personal iPhone), cloud backend knowledge (preferably GCP), daily collaboration, and an AI-driven development workflow, working 4 hours daily in the 9–5 ET window. Be aware this role demands intense communication, real-device testing with large media libraries, and rapid iteration cycles, so if you prefer solo deep work or lack the hardware/setup, it's not a fit.",
  "job_score": 94,
  "job_score_breakdown": {
    "categories": {
      "price":             { "raw_score": 18, "normalized_score": 18, "weight": 20 },
      "client_reputation": { "raw_score": 42, "normalized_score": 42, "weight": 42 },
      "client_spending":   { "raw_score": 24, "normalized_score": 24, "weight": 40 },
      "location":          { "raw_score": 10, "normalized_score": 10, "weight": 10 }
    },
    "top_contributors": [
      { "attribute": "price_min,price_max",    "value": "$70-$100/hr",       "score": 18, "category": "price" },
      { "attribute": "hire_rate",              "value": "100% (57 jobs)",    "score": 15, "category": "client_reputation" },
      { "attribute": "buyer_score",            "value": "4.93 (44 reviews)", "score": 15, "category": "client_reputation" },
      { "attribute": "avg_hourly_rate",        "value": "$55.35/hr",         "score": 15, "category": "client_spending" },
      { "attribute": "buyer_payment_verified", "value": true,                "score": 12, "category": "client_reputation" },
      { "attribute": "client_location",        "value": "United States",     "score": 10, "category": "location" },
      { "attribute": "total_spent",            "value": "$54,226.41",        "score": 6,  "category": "client_spending" },
      { "attribute": "total_hours",            "value": "473 hours",         "score": 3,  "category": "client_spending" }
    ],
    "penalties": []
  },
  "questions": [
    "Describe a React Native iOS app you worked on that was live in TestFlight or the App Store. What parts of the client/server flow did you personally build or maintain, especially around photo/video uploads, auth, sync, or backend job status?",
    "Do you personally have an iPhone with a large real photo/video library that you can use for testing? What model and how many photos/videos in your camera roll?",
    "How are you currently using AI as a developer?",
    "How do you prevent regression?"
  ]
}

job_score is the rule-based 0–100 rank. Category weights: price (20) + client_reputation (42) + client_spending (40) + premium (5) + location (10). Each category is capped at its weight, then summed and bounded to 0–100.

job_score_breakdown is always included and explains which fields contributed how many points. Use it to build UI that highlights why a job ranked high, or to debug your score-threshold filters. top_contributors is sorted descending; penalties lists negative contributions (e.g., unverified payment, low hire rate on a client with many jobs posted).

questions is the array of Upwork screening questions captured for the job — empty [] for ~83% of postings, 1–6 strings when the client included them. Feed it into your auto-proposal generator.

Empty/missing AI fields are returned as null (not every job goes through full AI enrichment).

For the full list of fields with types and operator support, see Complete Field Reference above.

📸 One snapshot per job

Every job here is captured once, when it first enters the index — typically within minutes of going live (median under 4) — and the record never changes after that. There is no re-scrape: a job that later collects 40 proposals, or closes, keeps the record it had in its first minutes.

Everything a client writes into a posting is exact as of that moment: title, description, budget, skills, screening questions. The client's history (total spent, hire rate, feedback score, jobs posted) is what the job page showed at posting time, the same numbers you would have weighed before bidding.

client_invites_sent is also an at-capture value: clients usually send invites while creating the job, so it tells you whether a posting started invite-first (>=1) or open to everyone (0). It does not track invites the client sent later. Counters that only mean something live — proposals received, interviews, whether the job is still open — are absent from both the output and the filters: a first-minutes snapshot would pin them near zero forever, and serving that as data is worse than not serving it.

If you need the current state of a job days after posting (has it been filled? at how many proposals?), a per-URL scraper fits better. This actor is built for the opposite moment: catching postings minutes after they appear, before the crowd arrives.

💰 Pricing

$2.00 per 1,000 results. No monthly subscription. No platform usage fees. Runs that return zero results cost nothing.

---

📖 Reference — Fields, Filters & Syntax

Everything below is the complete manual. Skim it when you're ready to build precise queries.

⚙️ Input Configuration

All parameters are optional.

Parameter	Type	Description
limit	number	Number of jobs to return (1-100, default: 10)
notifications_only	boolean	If true, only returns NEW jobs since last call

⚠️ Filter Quality — Read This Before Filtering

Upwork only requires title, description, skills, and price-type from the client. Almost everything else is optional, and clients fill it in inconsistently. If you filter on a field that's empty for most jobs, you exclude all the matching jobs that simply didn't supply the value — not just the non-matching ones.

Field density measured on the last 30 days (~135K postings):

• Always-filled (safe to filter): title, description, skills, price_type, category_name, subcategory_name, experience_level, qual_pref_english, qual_type, client_location, hire_rate, company_size, open_jobs (all ≥99%)
• Mostly filled (mild loss): buyer_contract_date (~82%), buyer_payment_verified (~78%), ai_explicit_mention_of_agency (~73%)
• Half-filled (use carefully): price / price_min / price_max (~40% — fixed-price jobs leave hourly bounds null and vice versa), buyer_score, buyer_feedback_count, total_spent, industry, engagement_label, ai_summary, ai_technical_skills (30-70%)
• Sparse (filter at your own risk): ai_urgency (~14%), ai_duration (~27%), ai_deadline (~12%), qual_portfolio_required (~7%), qual_rising_talent (~12%) — most jobs are not tagged

Recommendation: filter on dense fields, then use sparse fields for post-fetch sorting / scoring / display. Combining sparse filters compounds the loss multiplicatively.

📊 Available Filter Fields

You can filter jobs by 58 different fields across these categories:

📋 Essential Job Information

Core job details like title, description, pricing

Available fields: title, description, skills, price_type, price, price_min, price_max, category_name, subcategory_name, date_posted, url, keywords, exclude_keywords

📋 Job Requirements

Experience level, qualifications, and constraints

Available fields: experience_level, engagement_label, engagement_weeks, qual_min_hours_week, qual_min_success_score, qual_pref_english, qual_rising_talent, qual_portfolio_required, qual_type

📋 Client Information

Details about the client posting the job

Available fields: client_location, jobs_posted, hire_rate, avg_hourly_rate, open_jobs, total_spent, hires, active_hires, total_hours, industry, company_size, buyer_city, buyer_feedback_count, buyer_score, buyer_contract_date, buyer_payment_verified

📋 AI-Powered Insights

AI-generated analysis of job requirements and urgency

Available fields: ai_urgency, ai_duration, ai_deadline, ai_technical_skills, ai_inferred_technical_skills, ai_explicit_mention_of_agency, ai_clients_technical_understanding, ai_summary

📋 System Metadata

Internal tracking and system fields

Available fields: id, created_at, questions, job_score, job_score_breakdown

🔥 Most Popular Filters

Field	Type	Example	Description
skills	text	"React"	Filter by required skills
price_min	numeric	">=50"	Minimum hourly rate
buyer_score	numeric	">=4.0"	Client rating (1-5 stars)
experience_level	select	"Intermediate"	Required experience level
ai_urgency	select	"Urgent"	AI-detected project urgency
date_posted	date	">=2025-01-01"	Job posting date

Tip — filter server-side, not after the fact: every filter below is applied before limit picks the page, so narrowing costs you nothing and rows you would have discarded never enter your dataset. Comparison and range syntax works on any numeric field — "total_spent": ">=10000", "price": "500-2000" — and date_posted is publication time, so ">=10 minutes ago" means what it says.

🎛️ Filter Syntax Guide

Different field types support different filtering options:

📝 Text Fields (skills, title, description)

• Contains search: "React" → finds jobs mentioning React
• Case-insensitive: automatically handled
• Empty check: "=null" → finds jobs with empty/null values
• Non-empty check: "!=null" → finds jobs with content

🔢 Numeric Fields (price, price_min, buyer_score)

• Exact match: "1000" → exactly 1000
• Operators: ">=50", "<=100", ">25", "<75"
• Ranges: "500-2000" → between 500 and 2000 (inclusive)

✅ Boolean Fields (buyer_payment_verified, qual_rising_talent, qual_portfolio_required)

• True: "true" or "1"
• False: "false" or "0"

📅 Date Fields (date_posted, created_at)

• Relative age (easiest): "7d" = last 7 days, "24h" = last day, "2w" = last two weeks. A bare number means days: "3" = last 3 days.
• Exact date: "2025-01-15"
• Operators: ">=2025-01-01", "<=2025-12-31" — also work on relative ages ("<=7d" = older than a week)
• ISO format: "2025-01-15T10:30:00Z"

🎯 Select Fields (experience_level, ai_urgency)

• Exact match: "Intermediate", "Urgent"
• Case-sensitive: use exact values from field options (e.g. "Urgent", not "urgent" or "high")

📋 Complete Field Reference

Essential Job Information

Field	Type	Operators	Examples	Description
title	text	contains, =null, !=null	Build a React Dashboard with Real-time Analytics, Virtual Assistant for Email Management, Logo Design for Tech Startup	The job posting title as written by the client. Contains the main description of what work needs to be done.
description	text	contains, =null, !=null	We need an experienced React developer to build a ..., Looking for a Python expert who can integrate machine learning models ...	Full job description text as written by the client. Contains detailed requirements, expectations, and project scope.
skills	text	contains, =null, !=null	JavaScript, React, Node.js, Python, Machine Learning, TensorFlow	Comma-separated list of required skills and technologies for the job as specified by the client.
price_type	select	equals	Fixed-price, Hourly	How the job is priced: Fixed Price (one-time payment) or Hourly (paid per hour worked).
price	numeric	>=, <=, >, <, =, ranges	500, 1200, 50	The budget amount for the fixed-price job.
price_min	numeric	>=, <=, >, <, =, ranges	25, 50, 100	Lower end of the job's hourly rate band (Upwork jobs advertise a range like $10-35/hr). Hourly jobs only - fixed-price jobs carry their budget in price and leave this empty.
price_max	numeric	>=, <=, >, <, =, ranges	75, 150, 500	Upper end of the job's hourly rate band (Upwork jobs advertise a range like $10-35/hr). Hourly jobs only - fixed-price jobs carry their budget in price and leave this empty.
category_name	select	equals	Accounting & Consulting, Admin Support, Customer Service, Data Science & Analytics, Design & Creative, +7 more	Main category of the job (e.g., Web Development, Data Science, Design). Upwork's primary job classification.
subcategory_name	select	equals	3D Modeling & CAD, AI & Machine Learning, AI Apps & Integration, Accounting & Bookkeeping, Art & Illustration, +45 more	Specific subcategory within the main category (e.g., Front-End Development, Machine Learning). More granular classification.
date_posted	date	>=, <=, >, <, =, equals	7d, 24h, >=2026-05-01, 2026-05-17	When the job was posted on Upwork. Use this to find recent opportunities or analyze posting patterns.
url	text	contains, =null, !=null	https://www.upwork.com/jobs/~01234567890abcdef, https://www.upwork.com/jobs/~987654321fedcba09	Direct link to the job posting on Upwork. Use this to view the full job details or apply.
keywords	text	contains	python machine learning, react javascript typescript, design ui ux figma, marketing seo content, data analysis sql python	Full-text search across job title, description, skills, categories, location, industry, and AI-generated fields. Uses PostgreSQL full-text search for fast performance on millions of jobs.
exclude_keywords	text	contains	wordpress php, data entry copy paste, logo design graphic, social media marketing, excel spreadsheet manual	Exclude jobs containing any of these keywords. Uses the same full-text search across job title, description, skills, categories, location, industry, and AI-generated fields. Perfect for filtering out unwanted job types or technologies.

Job Requirements

Field	Type	Operators	Examples	Description
experience_level	select	equals	Entry_level, Expert, Intermediate	Required experience level for the job. Exact values: Entry_level, Intermediate, Expert.
engagement_label	select	equals	1 to 3 months, 3 to 6 months, Less than 1 month, More than 6 months	Expected duration or type of engagement (e.g., 1 to 3 months, 3 to 6 months, Less than 1 month, Less than 1 week, More than 6 months).
engagement_weeks	select	equals	3, 9, 18, 52	Project duration in weeks. Options: 1, 3, 9, 18, 52 weeks.
qual_min_hours_week	select	equals	0, 10, 30, 40	Minimum hours per week required for hourly jobs. Options: 10, 30, 40.
qual_min_success_score	select	equals	0, 80, 90	Minimum Upwork success score required to apply for the job.
qual_pref_english	select	equals	ANY, CONVERSATIONAL, FLUENT, NATIVE	Client's preferred English proficiency level for freelancers.
qual_rising_talent	boolean	equals	True, False	Whether the job is open to Upwork Rising Talent (newer freelancers with potential).
qual_portfolio_required	boolean	equals	True, False	Whether the client requires a portfolio or work samples to apply.
qual_type	select	equals	AGENCY, ANY, INDEPENDENT	Type of freelancer the client is looking for: Agency (team/company), Independent (solo freelancer), or Any (no preference).

Client Information

Field	Type	Operators	Examples	Description
client_location	text	contains, =null, !=null	United States, United Kingdom, Canada	Geographic location of the client posting the job.
jobs_posted	numeric	>=, <=, >, <, =, ranges	1, 10, >50	Total number of jobs the client has posted on Upwork.
hire_rate	numeric	>=, <=, >, <, =, ranges	75, 90, 50	Percentage of freelancers the client has hired from total applications. Higher rates indicate clients who actively hire rather than just browse.
avg_hourly_rate	numeric	>=, <=, >, <, =, ranges	45.50, 75.00, >=100	Average hourly rate this client typically pays freelancers. Based on their historical hiring patterns.
open_jobs	numeric	>=, <=, >, <, =, ranges	0, 2, <5	Number of jobs the client currently has open/active.
total_spent	numeric	>=, <=, >, <, =, ranges	100, 500, >=1000000	Total amount the client has spent on Upwork across all their projects.
hires	numeric	>=, <=, >, <, =, ranges	1, 10, >50	Total number of freelancers hired by the client.
active_hires	numeric	>=, <=, >, <, =, ranges	1, 10, >50	Number of freelancers currently hired by the client.
total_hours	numeric	>=, <=, >, <, =, ranges	100, 500, >=1000	Total number of hours the client has worked on Upwork across all their projects.
industry	select	equals	``, Aerospace, Agriculture & Forestry, Art & Design, Automotive, +15 more	Main industry the client operates in. Select from available choices.
company_size	select	equals	0, 1, 10, 100, 1000, +4 more	Size of the client's company. Predefined option for company size. Select from available choices.
buyer_city	text	contains, =null, !=null	New York, London, Paris, Tokyo, Sydney, Berlin, Rome, Madrid, Amsterdam, Mumbai, Beijing, Delhi, Mumbai	City where the client is located.
buyer_feedback_count	numeric	>=, <=, >, <, =, ranges	1, 10, >=2	Number of feedbacks the client has received.
buyer_score	numeric	>=, <=, >, <, =, ranges	>=4.7, 5.0	Score of the client's performance on Upwork.
buyer_contract_date	date	>=, <=, >, <, =, equals	>=2024-01-15, 2024-01-20T14:30:00Z, 365d	Date when the client first registered their account on Upwork. Indicates how long the client has been active on the platform.
buyer_payment_verified	boolean	equals	True, False	Boolean flag indicating whether the client has verified their payment method on Upwork.

AI-Powered Insights

Field	Type	Operators	Examples	Description
ai_urgency	select	equals	Immediate, Long-Term, Moderately Urgent, Not Urgent, Urgent, Very Urgent	AI-detected urgency level of the job based on language and posting patterns.
ai_duration	select	equals	Flexible, Flexible Deadline, Long-Term, Mid-Term, Part-Time, Short-Term	AI-detected project duration based on job description analysis. Indicates expected length and type of engagement.
ai_deadline	select	equals	Fixed Deadline, Flexible Deadline, Immediate Deadline, No Deadline	AI-detected deadline type for the job based on urgency indicators and time-sensitive language in the job description.
ai_technical_skills	text	contains, =null, !=null	JavaScript, React, Node.js, Python, Django, PostgreSQL, AWS, Docker, Kubernetes	Technical skills explicitly mentioned in the job description, extracted using AI. These are skills directly stated by the client as requirements or preferences.
ai_inferred_technical_skills	text	contains, =null, !=null	Git, REST APIs, Testing, Database Design, Security, Responsive Design, SEO	Technical skills inferred by AI from the job description context, even when not explicitly mentioned. These are skills likely needed based on project requirements and industry patterns.
ai_explicit_mention_of_agency	select	equals	Agencies Welcome, No Agencies, No Mention	AI-detected explicit mention of agency preferences in the job posting. Indicates whether the client welcomes agencies, prefers individual freelancers, or has no specific preference.
ai_clients_technical_understanding	select	equals	Expert, High, Low, Moderate	AI-detected assessment of the client's technical understanding based on how they describe their project requirements. Helps identify whether the client has deep technical knowledge, moderate understanding, basic knowledge, or expert-level expertise in the domain.
ai_summary	text	contains, =null, !=null	This job is a full-stack developer role for a startup. The ideal candidate will have experience with React, Node.js, and PostgreSQL., This job is a data analyst role for a consulting firm. The ideal candidate will have experience with SQL, Python, and data visualization tools.	AI-generated summary of the job description. Provides a concise overview of the job requirements and responsibilities.

System Metadata

Field	Type	Operators	Examples	Description
id	text	contains, =null, !=null	~01234567890abcdef, ~987654321fedcba09	Unique identifier for the job posting on Upwork. Use this to track specific jobs or avoid duplicates.
created_at	date	>=, <=, >, <, =, equals	2024-01-20T14:30:00Z	Timestamp when this job record was created in our system (not the Upwork posting date).
questions	array	(read-only, not a filter input)	['Name one long-tail keyword you would target for a UK mortgage and investment app and explain why?', 'Share a specific SEO or ASO result you have driven. Include the numbers?'], ['Are you willing to undergo a background check, in accordance with local law/regulations?', 'How soon can be available for work?', 'What is your level of proficiency in English?', 'Briefly describe your experience with Oracle Fusion HCM']	Upwork screening questions captured for the job, as written by the client. Empty array [] for ~83% of postings (jobs without screening questions). When present, contains 1-6 question strings. Feed straight into a proposal-answer generator to pre-write answers, or surface as an application checklist.
job_score	numeric	>=, <=, >, <, =, ranges	>=50, >=70, 60-90	Rule-based 0–100 score that ranks the job by attractiveness. Weighted across price (20), client reputation (42), client spending (40), premium status (5), and location (10). Because 82 of those 100 points come from client reputation and spending, a threshold mostly selects for good clients rather than good briefs.
job_score_breakdown	object	(read-only, not a filter input)	-	Always-included explanation of the job_score: per-category raw/normalized/weight, ranked top_contributors, and penalties. Use to surface why a job ranked high in UI, or to debug threshold filters. See Output Format below.

🎯 Smart Usage Patterns

💼 Freelancer Job Alerts

// High-value React jobs from quality clients
{
  "notifications_only": "true",
  "skills": "React",
  "price_min": ">=60",
  "buyer_score": ">=4.0",
  "buyer_payment_verified": "true",
  "ai_urgency": "Urgent"
}

// Remote-friendly design jobs
{
  "keywords": "design ui ux figma",
  "price_type": "Fixed-price",
  "price": "500-5000"
}

🏢 Agency Lead Generation

// Jobs explicitly welcoming agencies or teams
{
  "ai_explicit_mention_of_agency": "Agencies Welcome",
  "price_min": ">=100",
  "total_spent": ">=10000"
}

// Enterprise projects with team requirements
{
  "ai_explicit_mention_of_agency": "Agencies Welcome",
  "engagement_label": "More than 6 months",
  "company_size": "1000",
  "ai_clients_technical_understanding": "High"
}

// High-value development projects for agencies
{
  "ai_explicit_mention_of_agency": "Agencies Welcome",
  "keywords": "react node.js typescript",
  "price": ">=25000",
  "ai_duration": "Long-Term",
  "buyer_score": ">=4.5"
}

🔍 Market Research & Analysis

// Track AI/ML job trends and pricing
{
  "keywords": "machine learning artificial intelligence",
  "date_posted": ">=2025-01-01",
  "limit": 100
}

// Monitor mobile app development market
{
  "skills": "iOS Android React Native",
  "price_type": "Fixed-price",
  "date_posted": ">=2024-12-01",
  "experience_level": "Intermediate"
}

// Analyze client spending patterns
{
  "total_spent": ">=100000",
  "hire_rate": ">=90",
  "buyer_payment_verified": "true",
  "limit": 50
}

🎯 Specialized Niches

// Blockchain & crypto projects with quality clients
{
  "keywords": "blockchain cryptocurrency solidity ethereum",
  "exclude_keywords": "scam pyramid scheme",
  "ai_clients_technical_understanding": "High",
  "price_min": ">=50"
}

// Technical writing for clients who understand the work
{
  "keywords": "technical writing documentation",
  "ai_clients_technical_understanding": "Moderate",
  "experience_level": "Intermediate",
  "price_min": ">=30",
  "qual_pref_english": "FLUENT"
}

// Urgent fixes for immediate delivery
{
  "ai_urgency": "Immediate",
  "ai_deadline": "Fixed Deadline",
  "keywords": "bug fix maintenance urgent",
  "ai_duration": "Short-Term",
  "price_min": ">=40",
  "date_posted": ">=2025-01-01"
}

🚀 Notification & Automation

// Daily new job alerts for Python developers
{
  "notifications_only": "true",
  "skills": "Python",
  "price_min": ">=45",
  "buyer_score": ">=3.5",
  "limit": 20
}

// Weekend side project hunting
{
  "engagement_label": "Less than 1 month",
  "price_type": "Fixed-price",
  "price": "1000-10000"
}

⚡ Performance

• Typical run: ~8 seconds end to end for 100 results. Most of that is Apify container start-up — the query behind it returns in well under a second.
• Jobs are served from a pre-indexed store, not scraped live per run. That's why a 100-result run takes seconds rather than minutes, and why filters run against the whole corpus instead of one page of search results. Each job is indexed once and never re-scraped — see "One snapshot per job" above for what that means for the data.

🚨 Common Issues & Solutions

Issue: "No jobs returned"

• Solution: Check your filters aren't too restrictive
• Tip: Start with broader filters and narrow down

Issue: "Same jobs appearing"

• Solution: Use notifications_only: true for new jobs only
• Tip: Cursor tracking is automatic per user session

Issue: "Rate limit exceeded"

• Solution: Add delays between requests
• Tip: Use higher limit values to reduce request frequency

🔗 Integration Examples

Webhook Integration

// Set up webhook to receive new job notifications
const webhook = await apifyClient.webhooks().create({
  eventTypes: ['ACTOR.RUN.SUCCEEDED'],
  requestUrl: 'https://your-app.com/webhook/new-jobs'
});

Slack Bot Integration

// Post new jobs to Slack channel
const jobs = await actor.call({
  notifications_only: 'true',
  skills: 'React',
  price_min: '>=50'
});

jobs.items.forEach(job => {
  slack.postMessage({
    channel: '#job-alerts',
    text: `🚀 New ${job.skills} job: ${job.title} - $${job.price_min}+/hr`
  });
});

📊 Need historical data?

For research or model training, the live scraper isn't the right tool. We also publish a historical Upwork jobs dataset with 1.9M+ records from 2024–2025 in CSV, JSON, Parquet, SQLite, and DuckDB formats — with the same AI-enriched fields. See the Upwork Jobs Dataset on Gumroad for details.

🆘 Support

Found an issue or have a feature request? Open an issue on the Issues tab. We respond within ~48 hours.

Contact: apify@hyperbach.com