As part of this role, you will not only design and implement data labeling pipelines but also act as a trusted technical advisor for our customers - helping them understand their data needs, discover ...
As part of this role, you will not only design and implement data labeling pipelines but also act as a trusted technical advisor for our customers - helping them understand their data needs, discover ...
Freelance Data Labeling information
What is the difference between Freelance Data Labeling vs Data Annotation Specialist?
| Aspect | Freelance Data Labeling | Data Annotation Specialist |
|---|---|---|
| Credentials | No formal credentials required; experience and skills matter | Often requires knowledge of annotation tools and sometimes certifications |
| Work Environment | Remote, flexible, project-based | Typically employed by companies, may work remotely or on-site |
| Employer & Industry Usage | Freelance platforms, AI and machine learning projects | Tech companies, research institutions, AI firms |
Freelance Data Labeling involves independently completing labeling tasks for various clients, offering flexibility and diverse projects. Data Annotation Specialists are often employed by organizations to perform detailed annotation work, sometimes requiring specific certifications. Both roles support AI development but differ mainly in employment structure and credentials.
What are the key skills and qualifications needed to thrive as a Freelance Data Labeler, and why are they important?
To thrive as a Freelance Data Labeler, you need strong attention to detail, basic data analysis skills, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Experience with annotation platforms, image or text labeling tools, and sometimes knowledge of basic coding or spreadsheet software is beneficial. Reliability, time management, and effective communication are crucial soft skills for meeting deadlines and collaborating with remote teams or clients. These skills ensure the accurate and efficient preparation of high-quality labeled data, which is essential for training reliable machine learning models.
What is freelance data labeling?
Freelance data labeling involves annotating or tagging data, such as images, text, or audio, to prepare it for use in machine learning and artificial intelligence projects. As a freelancer, you may work with companies or platforms that need large amounts of accurately labeled data to train their algorithms. Tasks can include identifying objects in photos, transcribing audio, or categorizing text. This work is typically remote and flexible, making it suitable for individuals seeking part-time or project-based opportunities.
What are some common challenges faced by freelance data labelers, and how can they be managed?
Freelance data labelers often encounter challenges such as repetitive tasks, tight deadlines, and varying quality standards between clients. Managing these challenges involves maintaining clear communication with clients about expectations, using productivity tools to track progress, and regularly reviewing guidelines to ensure accuracy. Additionally, taking short breaks and organizing tasks can help prevent fatigue and maintain high-quality output. Building a network with other labelers can also provide support and tips for handling complex projects.
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Re-posted 2 days ago
Job description
Company Intro
At Toloka AI we create data that powers leading GenAI models and innovations. We work with frontier labs, big tech, renowned AI startups, enterprises and non-profit research organizations worldwide. We use a combination of Experts + Crowd + Tech Platform to teach AI models to reason and evaluate their efficacy and safety. We have experts in more than 50 different domains-from doctors and lawyers to physicists and engineers-and boast one of the most diverse global crowds, representing over 100 countries and speaking 40+ languages. We are a well-funded startup with an enviable portfolio of clients including Anthropic, Amazon, Microsoft, Poolside, Recraft, and Shopify.
Recently, we secured strategic investment led by Bezos Expeditions and Nebius Group with participation from Mikhail Parakhin, CTO of Shopify and board advisor to leading GenAI companies, who now serves as our Chairman of the Board. Our remote-first team is globally distributed around the world: USA, UK, the Netherlands, Serbia, and more.
About the Role
Technical consultants play a key role in our company, ensuring top-level delivery of our product, its correct configuration, and the deployment of tailored AI data solutions that meet our customers' needs and deliver an exceptional experience working with us.
As part of this role, you will not only design and implement data labeling pipelines but also act as a trusted technical advisor for our customers - helping them understand their data needs, discover new opportunities, and build scalable AI solutions.
We are looking for a person with more than 3 years of solid technical background, who feels comfortable working in technical environments, solving data-related tasks using Python and AI, and communicating directly with customers. You should be a confident communicator able to engage with both technical and executive stakeholders and collaborate closely with our commercial team.
- Act as the primary technical point of contact for customers across technical and executive stakeholders.
- Lead discovery conversations to deeply understand client goals, problems and quality criteria.
- Translate client needs into clear, actionable solution designs and delivery plans.
- Advise customers on best practices in data collection, data quality, evaluation, and model training workflows.
- Set expectations proactively, communicate progress regularly, and manage risks and trade-offs transparently.
- Identify expansion opportunities and additional ways Toloka can create value through data and AI solutions.
- Design end-to-end data solutions using Toloka's platform, selecting appropriate components, workflows, and quality controls.
- Configure data labeling components and quality controls, develop user interfaces and AI-driven tooling (e.g. agentic systems, RAGs, synthetic data generation), and integrate them into end-to-end automated pipelines for AI training and evaluation.
- Run experiments and pilots to validate quality, throughput, and cost assumptions.
- Lead technical execution during delivery, coordinating internal teams and external stakeholders as needed.
- Ensure solutions meet agreed quality, performance, and scalability requirements.
- Own delivery outcomes holistically - including technical performance, quality, timelines, and financial efficiency.
- Monitor key KPIs (e.g. throughput, quality metrics, Gross Margin, Contribution Margin) and act on deviations.
- Prioritize work based on customer impact and business value.
- Analyze data to identify failure modes, inefficiencies, and opportunities for improvement.
- Continuously optimize solutions through iteration, automation, and adoption of new technologies.
- Troubleshoot delivery, process, or technical issues in collaboration with support, infrastructure, and product teams.
- Drive solutions toward stable operation with minimal ongoing maintenance.
- Experience as a Solution Engineer, Technical Solutions Engineer, Data Scientist, Data/Systems Analyst, or Technical Project Manager.
- Proven experience owning projects end-to-end, including scoping, delivery, and stakeholder management.
- Direct experience working with external customers on technical projects (requirements gathering, delivery, iteration).
- Strong Python skills for data manipulation (e.g. NumPy, pandas), natural language processing (e.g. NLTK, spaCy), and building lightweight web services or tools (e.g. FastAPI, Flask).
- Solid understanding of ML concepts and data workflows; familiarity with LLMs and prompt engineering is a strong plus.
- Experience designing multi-stage pipelines and complex systems.
- Understanding of software development fundamentals: version control, testing, MVPs, iterative development etc..
- Bachelor's or Master's degree in Data Science, ML/AI, Software Engineering, or a related quantitative field such as Physics or Applied Mathematics, with strong analytical foundations.
- Ability to reason about trade-offs between quality, speed, cost, and scalability.
- Excellent English communication skills (B2+), with the ability to explain complex technical concepts to non-technical audiences.
- Confidence working with data engineers, ML engineers, and researchers from leading tech companies.
- Strong ownership mindset: comfortable being accountable for outcomes, not just tasks.
- Ability to manage multiple projects in fast-paced, ambiguous environments.
- Experience with crowdsourcing, expert workflows, or large-scale data labeling.
- Experience working on ML training, evaluation, or agentic systems.
- Familiarity with quality frameworks, audits, or data validation pipelines.
[Important Notice] Scam Alert Regarding Fake Job Postings
It has come to our attention that an individual or group is fraudulently impersonating Toloka to post fake jobs and solicit personal information from applicants.Please be aware:
- Official Communication: Our recruiting team will only contact you from an official "toloka.ai" email address. We will NEVER use Gmail, Yahoo, Tolokainc, toloka.inc, or other personal or seemingly business email accounts.
- Our Process: We will never ask for your bank account details, credit card number, or any fees as part of the application or interview process.
- Official Listings: All legitimate job openings are posted on our official careers page: https://toloka.ai/careers#job-list
What to do: If you see a suspicious job posting or have been contacted by someone you suspect is a scammer, please do not provide any personal information. Instead, report the incident to us directly at security@toloka.ai and report the profile/post to LinkedIn.We are taking this matter very seriously and are working with the appropriate parties to resolve it.
Thank you for your vigilance!
Thank you for your vigilance!