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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 We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks. Essentially, we're building an olympiad for AI models: the better the tasks, the more precisely we see where models are weak, and the faster they improve. What you'll actually do You create tasks and the criteria to evaluate them. Each task consists of: An isolated environment - an emulation of a developer's workstation: a Linux machine with dev tools (terminal, CLI), MCP servers (repository, task tracker, messenger, documentation, etc.), and a real web application codebase; A task - a prompt, just like in real work (a ticket link, a messenger conversation, anything). The agent figures it out on its own - reads the ticket, checks discussions, searches docs, explores the code. All of that context is also part of the task and needs to be created and thought through; Tests and evaluation criteria - how to verify the agent solved the task correctly. The work breaks down into two phases: Building the virtual company - following a high-level plan, you create a company: codebase, infrastructure, context (conversations, documentation, tickets). The result is a realistic environment with development history. Assembling and calibrating tasks - from intermediate states of the company, task drafts are assembled (based on a taxonomy). You turn a draft into a full task: write the prompt, define evaluation criteria, ensure the task is solvable and the evaluation is fair. Then an AI agent receives the task and tries to solve it the way a developer would - and you evaluate the result. What the work looks like in practice: Iterating with an AI agent on tests - they should catch real problems, not miss bad solutions, not break on good ones; Reviewing code written by agents; Analyzing why an agent failed or succeeded; Designing edge cases and adversarial scenarios. A significant part of the work is done together with AI - it's very hard to create tasks that are hard for frontier models without using frontier models. Why this is hard Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial - you need to understand where models fail and what reveals the difference between a good and a bad solution. Tasks have many valid solutions. Tests must be neither too strict (breaking valid solutions) nor too lenient (passing bad ones). Core tech stack The virtual companies are built around a typical full-stack web application: Backend: Python, FastAPI Frontend: JavaScript/TypeScript, React Infrastructure: Docker, Postgres, Kafka, Redis You don't need to be an expert in every item, but you should be comfortable reading and reasoning about code across the stack - that's what lets you design realistic tasks, write meaningful tests, and evaluate results. Growth You start with task and dataset work, and if it goes well, we expect you to move into a project team - with a broader scope and deeper involvement. Requirements Strong software engineering experience: 8+ years in any modern programming language, with a willingness and ability to work primarily in Python in our projects; Hands-on experience using coding agents such as Claude Code and Codex as part of practical engineering workflows, while maintaining ownership of architecture, correctness, code quality, and delivery; Problem-solving and clarity of thinking; Ability to communicate technical decisions and reasoning; Practical understanding of Docker and development workflows; Overall readiness to contribute in a technical, execution-focused role. What we can offer Freelance collaboration via the Ontop freelance platform. Flexible, fully remote schedule ( 40 hours per week ). You’ll have the opportunity to work on highly innovative projects at the leading edge of AI development, together with a genuinely dedicated and dynamic team of experts. You’ll also be working on projects with customers that are AI industry leaders and well-known household names. Friendly community. [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!
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.