Providing Computer Vision Infrastructure for Project Stormcloud

With increasing reliance on computer vision (CV) systems in multiple industrial domains, the demand for robust data annotation solutions is rising exponentially. The most recent reports project the data annotation tools market to have a compound annual growth rate (CAGR) of 21.8% from 2024 to 2032. However, as several companies emerge offering annotation platforms and services, finding a cost-effective provider is challenging. While many platforms offer advanced annotation features, only a few meet the scalability and security requirements essential for enterprise-level CV applications. This article discusses the ten best video and image annotation companies in 2024 to help you with your search. The following lists the companies we think are driving the data annotation space: Encord iMerit Appen Label Your Data KeyMakr TrainingData SuperbAI Kili Technology Telus International SuperAnnotate CogitoTech LabelBox Top 12 Data Annotation and Data Labeling Companies Data annotation companies offering labeling solutions must meet stringent security and scalability requirements to match the high standards of the modern artificial intelligence (AI) space. Below are the twelve top companies, ranked based on the following factors:  Data security protocols: Compliance with data security regulations and use of encryption algorithms. Scalability: The solution’s ability to handle large data volumes and variety. Collaboration: Tools allowing different team members to collaborate on projects. Ease of use: A user-friendly interface that is intuitive and easy to navigate. Supported data types: support for different modalities such as video, image, audio, and text. Automation: AI-based labeling for speeding up annotation processes. Other functionalities for streamlining the annotation workflow include integration with cloud services and advanced annotation methods for complex scenarios. Let’s explore each company's annotation platforms or services and see the key features based on the above factors to help you determine the most suitable option. Encord Encord is an end-to-end data platform that enables you to annotate, curate, and manage computer vision datasets through AI-assisted annotation features. It also provides intuitive dashboards to view insights on key metrics, such as label quality and annotator performance, to optimize workforce efficiency and ensure you build production-ready models faster. SOTA Model-assisted Labeling and Customizable Workflows with Encord Annotate Key Features Data security: Encord complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards. It uses advanced encryption protocols to ensure data security and privacy. Scalability: The platform allows you to upload up to 500,000 images (recommended), 100 GB in size, and 5 million labels per project. You can also upload up to 200,000 frames per video (2 hours at 30 frames per second) for each project. See more guidelines for scalability in the documentation. Collaboration: You can create workflows and assign roles to relevant team members to manage tasks at different stages. User roles include admin, team member, reviewer, and annotator. Ease-of-use: Encord Annotate offers an intuitive user interface (UI) and an SDK to label and manage annotation projects. Supported data types: The platform lets you annotate images, videos (and image sequences), DICOM, and Mammography data. Supported annotation methods: Encord supports multiple annotation methods, including classification, bounding box, keypoint, polylines, and polygons. Automated labeling: The platform speeds up the annotation with automation features, including: - Segment Anything Model (SAM) to automatically create labels around distinct features in all supported file formats. - Interpolation to auto-create instance labels by estimating where labels should be created in videos and image sequences. - Object tracking to follow entities within images based on pixel information enclosed within the label boundary. Integration: Integrate popular cloud storage platforms, such as AWS, Google Cloud, Azure, and Open Telekom Cloud OSS, to import datasets. Best for Teams looking for an enterprise-grade image and video annotation solution to produce high-quality data for computer vision models. Pricing Encord has a pay-per-user pricing model with Starter, Team, and Enterprise options. Learn more about automated data annotation by reading our guide to automated data annotation. iMerit iMerit offers Ango Hub, a data annotation solution built on a generative AI framework that lets you build use-case-specific applications for autonomous vehicles, agriculture, and healthcare industries. iMerit Key Features Collaboration: The Ango Hub solution lets you add labelers and reviewers to customized workflows for managing annotation projects. Ease-of-use: The platform offers an intuitive UI to label items, requiring no coding expertise. Supported data types: Ango Hub supports audio, image, video, DICOM, text, and markdown data types. Supported labeling methods: The solution supports bounding boxes, polygons, polylines, segmentation, and tools for natural language processing (NLP). Integration: The platform features integrated plugins for automated labeling and machine learning models for AI-assisted annotations. Best for Teams searching for an integrated labeling platform for annotating text, video, and image data. Pricing Pricing information is not publicly available. Contact the team to get a quote. Appen Appen offers data annotation solutions for building large language models (LLMs) by providing a standalone labeling platform and data labeling services through expert linguists. Appen Key Features Workforce capacity: Appen’s managed services include more than a million specialists speaking over 200 languages across 170 countries. With the option to combine its platform with its services, the solution becomes highly scalable. Supported data types: Appen’s platform lets you label documents, images, videos, audio, text, and point-cloud data. Supported annotation methods: Labeling methods include bounding boxes, cuboids, lines, points, polygons, ellipses, segmentation, and classification. Instruction datasets: The company also offers domain-specific instruction datasets for training LLMs. Best for Teams looking for a hybrid solution for building multi-modal models for text and vision applications. Pricing Pricing is not publicly available. Label Your Data Label Your Data is a data annotation service provider offering video and image annotation services for CV and NLP applications. Label Your Data Key Features Data security: The company complies with ISO 27001, GDPR, and CCPA standards. Workforce capacity: Label Your Data builds a remote team of over 500 data annotators to speed up the annotation process. Supported data types: The solution supports image, video, point-cloud, text, and audio data. Supported labeling methods: CV methods include semantic segmentation, bounding boxes, polygons, cuboids, and key points. NLP methods include named entity recognition (NER), sentiment analysis, audio transcription, and text annotation. Best for Teams looking for a secure annotation service provider for completely outsourcing their labeling efforts. Pricing Label Your Data provides on-demand, short- and long-term plans. Keymakr Keymakr is an image and video annotation service provider that manages labeling processes through its in-house professional experts. Keymakr Key Features Labeling capacity: You can label up to 100,000 data items. Supported data types: The platform supports image, video, and point-cloud data. Supported labeling methods: Keymakr offers annotations that include bounding boxes, cuboids, polygons, semantic segmentation, key points, bitmasks, and instance segmentation. Smart assignment: The solution features a smart distribution to match relevant annotators with suitable tasks based on skillset. Performance tracking: Keymakr provides performance analytics to track progress and alert managers in case of issues. Data collection and creation: The company also offers services to create relevant data for your projects or collect it from reliable sources. Best for Beginner-level teams working CV projects, requiring data creation and annotation services. Pricing Pricing is not publicly available. TrainingData TrainingData is a Software-as-a-Service (SaaS) data labeling application for CV projects, featuring pixel-level annotation tools for accurate labeling. TrainingData Key Features Data security: The company provides a Docker image to run on your local network through a secure virtual private network (VPN) connection. Scalability: You can label up to 100,000 images. Collaboration: TrainingData’s platform lets you create projects and add relevant collaborators with suitable roles, including reviewer, annotator, and admin. Supported labeling methods: The platform offers multiple labeling tools, including a brush and eraser for pixel-accurate segmentation, bounding boxes, polygons, key points, and a freehand drawer for freeform contours. Integration: TrainingData integrates with any cloud storage service that complies with cross-origin resource sharing (CORS) policy. Best for Teams looking for an on-premises image annotation platform for segmentation tasks. Pricing TrainingData offers free, pro, and enterprise packages. SuperbAI SuperbAI offers multiple products for building AI models, including a data management platform, a labeling solution, and a tool for training, evaluating, and deploying models. SuperbAI Key Features Data security: SuperbAI complies with SOC standards and encrypts all data using Advanced Encryption Standard - 256 (AES-256). Collaboration: The platform offers access management tools and lets you invite team members as admins, labelers, and managers. Supported data types: SuperbAI supports images and videos in PNG, BMP, JPG, and MP4 formats. It also supports point-cloud data. Supported labeling methods: The solution supports all standard labeling methods, including bounding boxes, polylines, polygons, and cuboids. Integration: The platform integrates with Google Cloud, Azure, AWS, and Slack. Best for Teams looking for an integrated data management solution for training machine learning algorithms. Pricing SuperbAI offers starter and enterprise packages. Kili Technology Kili Technology offers an intuitive labeling platform to annotate data for LLMs, generative AI, and CV models with quality assurance features to produce error-free datasets. Kili Technology Key features Collaboration: The platform lets you assign multiple roles to team members, including reviewer, admin, manager, and labeler, to collaborate on projects through instructions and feedback. Ease-of-use: Kili offers a user-friendly UI for managing workflows, requiring minimal code. Supported labeling methods: The tool supports bounding boxes, optical character recognition (OCR), NERs, pose estimation, and semantic segmentation. Automation: Kili supports automated labeling through active learning and pre-annotations using ChatGPT and SAM. Best for Data scientists looking for a lightweight annotation solution for building generative AI applications. Pricing Pricing depends on the number of items you need to label. Telus International Telus International’s Ground Truth (GT) studio offers three platforms as part of a managed service to build training datasets for ML models. GT Manage helps with people and project management; GT Annotate lets you annotate image and video data. GT Data is a data creation and collection tool supporting multiple data types. Telus International Key Features Data security: GT Annotate complies with SOC 2 standards and implements two-factor authentication with firewall applications and intrusion detection for data security. Collaboration: GT Manage features workforce management tools for optimal task distribution and quality control. Supported data types: You can collect image, video, audio, text, and geo-location data using GT data. Supported labeling methods: GT Annotate supports bounding boxes, cuboids, polylines, and landmarks. Best for Teams looking for a complete AI solution for collecting, labeling, and managing raw data. Pricing Pricing information is not publicly available. SuperAnnotate SuperAnnotate offers a data labeling tool that lets you manage AI data through collaboration tools and annotation workflows while providing quality assurance features to produce labeling accuracy. SuperAnnotate Key Features Collaboration: SuperAnnotate lets you create teams and assign relevant roles such as admin, annotator, and reviewer. Ease-of-use: The platform has an easy-to-use UI. Supported data types: SuperAnnotate supports image, video, text, and audio data. Supported labeling methods: The platform has tools for categorization, segmentation, pose estimation, object tracking, sentiment analysis, and speech recognition. Best for Teams looking for an annotation solution to build generative AI applications. Pricing The platform offers free, pro, and enterprise versions. Cogito Cogito is a data labeling service provider that employs a large pool of human annotators to deliver annotations for generative AI, CV, content moderation, NLP, and data processing. Cognito Key Features Data security: Cogito complies with GDPR, SOC 2, HIPAA, CCPA, and ISO 27001 standards. Supported data types: The platform supports image, video, audio, text, and point-cloud data. Automation: Cogito uses AI-based algorithms to label large data volumes. Best for Startups looking for a company to outsource their AI operations. Pricing Pricing is not publicly available. Labelbox Labelbox offers multiple products for managing AI projects. Its data labeling platform allows you to annotate various data types for building vision and LLM applications. LabelBox Key Features Data security: Labelbox complies with several regulatory standards, including GDPR, CCPA, SOC 2, and ISO 27001. Collaboration: Users can create projects and invite in-house labeling team members with relevant roles to manage the annotation workflow. Ease-of-use: Labelbox has a user-friendly interface with a customizable labeling editor. Automation: The platform supports model-assisted labeling (MAL) to import AI-based classifications for your data. Integrability: Labelbox integrates with AWS, Azure, and Google Cloud to access data repositories quickly. Best for Teams looking for labeling solutions to build applications for e-commerce, healthcare, and financial services industries. Pricing Labelbox offers free, starter, and enterprise versions. Still confused about whether to buy a tool or go for open-source solutions? Read some lessons from practitioners regarding build vs. buy decisions Data Annotation Companies: Key Takeaways CV applications are driving the current industrial landscape by innovating fields like medical imaging, robotics, retail, etc. However, CV’s rapid expansion into these domains calls for robust data annotation tools and services to build high-quality training data. Below are a few key points regarding data annotation companies in 2024. Security is key: With data privacy regulations becoming stricter globally, companies offering annotation solutions must have compliance certifications to ensure data protection. Scalability: Annotation companies should offer scalable tools to handle the ever-increasing data volume and variety. Top annotation companies in 2024: SuperAnnotate, Encord, and Kili are the top 3 companies that provide robust labeling platforms and services.

"Say you want to watch a movie. To choose, you'll want to know what movies others liked and, based on what you thought of other movies you've seen if this is a movie you'd like. You'll be able to browse that information. Then you select and get video on demand. Afterward, you can even share what you thought of the movie. But thinking of it only in terms of movies on demand trivializes the ultimate impact. The way we find information and make decisions will be changed. Think about how you find people with common interests, pick a doctor, and decide what book to read. Right now, reaching out to a broad range of people is hard. You are tied into the physical community near you. But in the new environment, because of how information is stored and accessed, that community will expand. This tool will be empowering, the infrastructure will be built quickly and the impact will be broad." - The Bill Gates Interview, Playboy Magazine, July 1994 Sound familiar? Asked what else the personal computer was supposed to do other than process documents, Bill Gates prophesied the changes brought about by the coming of the information age that modern-day tech giants have since realized. From video-on-demand and movie recommendations (Netflix) to the way we find information (Google) to how you find people with common interests (Facebook) and deciding what book to read (Amazon 1.0), Gates' vision of the transformation that the information age would bring about turned out in more ways than one could conceivably imagine at the dawn of the Internet revolution.  The Coming of the AI Revolution Fast forward 20 years, the AI revolution has begun. It will fundamentally transform our world, much just like the advent of the atomic bomb, microprocessor, personal computer, and the Internet. If the wealth generated from the emergence of each of these technologies offers any indication, we are poised to witness an unprecedented accumulation of wealth. As with any prophesied significant platform shift, there's a real risk that they fail to materialize in a big way at a particular moment in time (e.g. Web3, Blockchain, Crypto, Metaverse) or that they take much longer than anticipated to play out (admittedly, crypto can still find an actual use case). Until as recently as ten years ago, almost all AI systems failed to demonstrate significant value, and many still do not (e.g., purely logic-based AI systems and symbolic AI - the dominant paradigm from the 1950s to the mid-1990s - are still primarily research interests). We could be in for another AI hype cycle that may eventually fizzle. As an eternal optimist and founder of an AI company looking to raise a Series B in the not-too-distant future, I won't bother spelling out why AI is overrated. Instead, I'll argue why it will change the world. When I explain AI to my parents, I describe it as a new form of dynamic software built on answers, unlike traditional status software built on rules. Put simply, comparing AI to conventional software is like saying "show" instead of "tell." What's exciting about AI is that dynamic and answer-based software will enable us to create new products, applications, and systems that can solve unsolved problems that, until now, have been reserved for human cognition. Self-driving cars are the most obvious example - while traditional software can handle simple tasks like driving straight, building a fully autonomous vehicle would require an overwhelming number of static rules to cover even the basics of navigation. It is not a leap to believe that the total addressable market (TAM) of problems only solvable by human cognition is orders of magnitude higher than that of any of the problems for which we use traditional software. As AI can augment and - in some cases - replace humans, it can produce what I think of as "non-linear" productivity outcomes. Here are a few contrived examples across various vertical use cases to illustrate the potential non-linearity of AI systems: Building a faster car to reduce the amount of attention required to drive from A to B (linear) vs. self-driving vehicle (non-linear) More efficient organization of leads and tasks in a CRM system with a slightly better UI for salespeople (linear) vs. AI talking avatars that allow for infinite scaling of the salesperson (non-linear) Improved diagnostic equipment that provides more detailed images for  radiologists to analyze (linear) vs. AI-driven systems that scan medical images and highlight potential anomalies for doctors or even predict possible illnesses before symptoms manifest based on health data (non-linear) Better tractors and machinery to help farmers plant and harvest crops (linear) vs. drones and robots that monitor the health of individual plants, apply precise amounts of fertilizer or pesticide, and harvest crops with minimal human intervention (non-linear) A digital learning and education platform with improved video lectures and homework targeted specifically at programming (linear) vs. an adaptive chatbot that can be prompted to "explain this concept like I'm 12 years old" (non-linear) The digital learning example is interesting as it is playing out in real-time: Chegg, the education technology company, saw its stock price tumble 47% (down ~63% year-to-date) after admitting that ChatGPT was pressuring its subscriber growth, leading them to suspend their full-year outlook. You get the idea. Just as the Internet's value skyrocketed with evolving applications, tools, and increased user participation, so too will the AI sector's worth. Despite the Internet's basic components remaining similar to those of the early 1990s, its value has grown exponentially over 20 years due to expanded applications and user engagement. As more individuals and businesses embrace AI and develop applications, the supporting tools and infrastructure will improve. Increased data availability will also enhance product quality. This cyclical improvement will fuel exponential growth in AI. Undoubtedly, AI is poised to be the next major technological platform shift within the next 20 years. While previous technological revolutions, like the Internet or the Industrial Revolution, were monumental in reshaping societies and economies, AI encapsulates something far more profound: the essence of human cognition. The wealth generation from novel solutions to previously unsolvable problems, combined with the heightened productivity and efficiency across all sectors, implies that the economic impact of AI could dwarf that of all prior technological shifts. The emergence of AI will give birth to an unfathomable number of unicorns. Who Wins: Titans, Challengers, or Innovators? Ok, so AI will be huge, but who wins the biggest slice of the pie, and where will the most value be generated? While the Twitter VC community may have its own predictions, here are my thoughts on a potential outcome. I could of course be entirely wrong. Early winners like NVIDIA have already experienced a surge in their stock price, and investors believe that generative AI and LLM developers will be the next big thing, as evidenced by the high valuations and significant investment flowing into those companies. The landscape is already fiercely competitive, especially among "neo" foundation model/LLM providers (e.g., Cohere, Anthropic, Mistral). Given the high valuations and evolving competitive landscape, I question the viability of venture-scale returns for most of these new entrants. There is a limit to how many chatbots the market can absorb, after all. Additionally, OpenAI is also so far ahead (8 years of R&D and billions of queries via ChatGPT generating valuable RLHF data) that it will be difficult for any of these companies to catch up. Perhaps one or two will succeed, but for emerging LLM companies to truly thrive, they will likely need to uncover unique niches or pivot towards refining larger models using specialized, proprietary datasets for distinct needs and/or partnering with downstream application developers. Some corporate/venture combinations could also happen, where partnerships like OpenAI/MSFT, Anthropic/Google, and Cohere/Meta-type will combine distribution and data advantage with R&D expertise. Elad Gil made some interesting observations on this here. Separately, foundation model providers will likely realize lower margins in the first few years of operation, as they more closely resemble 'hardware-type companies' with significant upfront training expenses. However, this does not mean that developers who create applications using these large models won't achieve considerable margins even if the TAM of those markets is much smaller, for example, by offering specialized expertise, products, and other value-added services related to these models. Jasper is an example of a company that has done this perfectly - they've built a product that serves marketers, and just marketers, well. All things considered, the AI market will probably resemble that of the current software market in 20 years. There will be a few huge "Big AI" companies with over $100 billion in revenue (this could very well end up being the foundation model developers, but it could also be companies that we haven't even conceived of yet) and a diaspora of large companies focusing on specific applications (e.g., Stripe for payments, Uber for transportation, Figma for design - this could be Jasper for marketers, Cruise for autonomous vehicles, Viz AI for medical imagery, and so on). For context, Apple, Microsoft, Amazon, Meta, and Alphabet constitute ~$9 trillion of the value of the NASDAQ's ~$22 trillion market cap. This is a substantial chunk, no doubt, but the total size of the pie is undoubtedly only going to get bigger as the AI market gets underway and secular trends in technology continue to reverberate. Why This Time is Different Technological revolutions often occur due to a convergence of pivotal factors, and the AI sector is currently experiencing such a juncture. Similar to the Internet's ascendance, which was enabled by ubiquitous personal computers and faster connectivity, the current AI boom is a product of simultaneous advances in computing power, vast data availability, and increasingly advanced models. Eric and I founded Encord at a pivotal moment when object detection models transitioned from often being erroneous and requiring highly controlled "sandbox"-type environments to delivering tangible ROI. Similarly, the release of ChatGPT marked a paradigm shift in how we approached natural language processing and understanding.  Looking into the near future, I anticipate AI delving deeper into multi-modal applications, offering higher ROI and increasingly viable solutions to more complex problems, and even stepping into realms of human reasoning. In short, the market is just getting started. The value, revenue, TAM, etc., will naturally accrue as the complexity of the problems that we solve with AI increases. After all, it was impossible to stream a movie over your Internet connection 20 years ago, but now its table stakes.

We sat down with Denis Gavrielov, Full Stack Engineer at Encord, to learn more about his day-to-day. Denis is an essential player on the Engineering team, he has been a part of Encord's exciting journey from a small 8-person team to the dynamic startup it is today. He walks us through the highs and lows of his experiences, the camaraderie he shares with his team, and the thrill of working on some truly fascinating projects. If you've ever wondered what it's like to work at a startup like Encord, strap in and join us for this conversation! Denis, first question. What inspired you to join Encord? I applied about two years ago when the team was still getting started (we were eight people strong — all engineers!). At that time, I wasn't sure if I wanted to join a startup as I had been working in a larger company up until then (Bloomberg). I met most of the team throughout the interview process, and the more I spoke to everyone, the more I felt I'd really enjoy working with them. I also had a great first impression of the founders (Eric & Ulrik) and the more I spoke with them the more I got a sense for how strong their skillset was and how unique and unparalleled of an opportunity it'd be. The combination of team, founders, and opportunity is what ultimately led me to join Encord. I still remember my first week sitting in our 10m2 office space in the middle of Soho without any meeting rooms or kitchen, quite different from today! Very different from now indeed! What does a typical day as a full-stack engineer at Encord look like? Every day is a bit different. Some people like to focus on back-end engineering and some like front-end work — I like to work on both! Typically, I start by catching up with everyone's messages on Slack. I might have one or two meetings in the morning before starting to code. Throughout the day there's a lot of collaboration via impromptu short catch-ups in person or on Slack. During these meetings, I often discuss architectural designs and problem-solving strategies with more senior team members. A significant component of my day is spent coding and solving problems. And I guess you fit in lunch in between that as well, right? That's true, a good lunch break is important for recharging! I usually grab lunch with a few colleagues — if the weather is nice, like during the summer, we enjoy our lunch outside. On Fridays, the entire team gets together in the office for a company lunch, that's always fun and one of my favorite Encord traditions. You mentioned teamwork and customer centricity — how would you describe the company culture at Encord more broadly? The company culture is very collaborative. The founders and senior managers are encouraging and open to suggestions, so it's easy to take (good) decisions independently. We try to maintain a relaxed office culture focused on team success, where people are free to work in a manner that works best for them and for us as a team more broadly. People are always trying to help each other out and break down any silos there might arise — although we've been very intentional though about building the right environment, so luckily there are very little silos in the first place. For example, our engineering and commercial team all work in one big room and getting context or feedback is incredibly quick. Another key part of our culture is being able to adapt quickly. Previously, I was working at Bloomberg in an infrastructure team, where we had to plan everything very precisely. Everything had to be correct from the start, and every decision would need to go through many layers of approval and planning sessions. Speed of progress was naturally quite different. Here at Encord, we are a nimble, rapid team — we move quickly and have been able to achieve things that teams many times our size have struggled with. Customers tell us every week how much better our product is compared to other alternatives they're looking at. It is always very rewarding when we hear this feedback. What we aim for as a team is to be great — ship high-performing industry-leading products quickly, get feedback from customers and prospects early, and consistently focus on building what customers and users really want. I think these are the two principles that stand out to me as an engineer the most. Can you tell us a bit about a project that you're currently working on? One of the projects I'm working on is overhauling of our task management system. The core problem we're trying to solve is that our customers need a detailed overview and a flexible interface to control the annotation and review process for their data operations. The process can be complicated, involving multiple review stages and different levels of scrutiny. So we're developing a system to allow our clients to implement more complex workflows and be able to review the process even faster! In terms of how I spend my time between collaborating and individual development, it varies heavily by what point of the project we're in. At the beginning, a large part of my time is spent brainstorming, talking through the objectives with other engineers and product managers, and listening in on client calls. After gathering ideas and refining our approach, I spend most of my time coding and focusing on the project — while still staying in sync with the rest of the team so that we keep moving quickly in the same direction. Lastly, what advice would you give to someone considering working at Encord? Many people reach out to me each week asking this and there's a few things I think many people don't consider. Encord is obviously in a very exciting position — we're a strong team, in the right market (AI infrastructure), and have a clear vision of what the future will be like and what we need to build to get there. Yet early-stage startups, especially ones moving very quickly, require working-modes and dispositions that the vast majority of people are not looking for nor comfortable with — and that's okay! If you don’t particularly enjoy collaborating on tasks, or you prefer being told very precisely/prescriptively what to do, or you're not at the stage in your career where you want to own processes, then I'd say Encord (and similar-type companies) are probably not the best fit. It really depends on what excites you. Here, you need to embrace pace, ownership, collaboration, and autonomy, often to degrees you may not have considered possible. These are key traits that, I think, have all made us successful in our roles. If you want to own problem areas and find solutions, if you like collaborating and prototyping MVPs quickly to get client feedback early, and if you pride yourself in making the right decisions with limited amounts of information — then Encord may be a good fit! Thank you, Denis! We have big plans for 2023 & are hiring across all teams. Find here the roles we are hiring for!