Identify trending open-source AI models with strong community adoption, import them into the Clarifai Community, and validate them across real-world use cases. Create clear, engaging previews and demos both technical and non-technical that showcase model capabilities. Collaborate with Marketing to promote new models and generate compelling content around them. Engage with the open-source AI community to build relationships with original model authors and increase backlink visibility. Develop lightweight Python-based demos and utilities to highlight model performance and usability. Impact As an ML Community Ops Engineer, you will directly contribute to growing Clarifai s model ecosystem by adding cutting-edge AI models and making them accessible to users. Your work will expand Clarifai s reach, improve discoverability, and ensure our platform remains at the forefront of open-source AI. By bridging engineering, marketing, and community engagement, you ll help solidify Clarifai s presence in the AI developer ecosystem. Requirements Strong experience developing, fine-tuning, and evaluating machine learning models, including familiarity with model architectures and key evaluation metrics. Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and architectures such as transformers and CNNs. Actively follows AI and ML trends staying current with emerging models, benchmarks, and communities. Proficiency in Python, with ability to write clean, efficient code for ML workflows and data pipelines. Experience working with cloud platforms (e.g., AWS, GCP, Azure) for model deployment and compute orchestration. Solid software engineering fundamentals, including Git, modular design, and code testing. Practical experience with data preprocessing, feature engineering, and analysis of large datasets. Great to Have Strong experience developing, fine-tuning, and evaluating machine learning models, including familiarity with model architectures and key evaluation metrics. Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and architectures such as transformers and CNNs. Actively follows AI and ML trends staying current with emerging models, benchmarks, and communities. Proficiency in Python, with ability to write clean, efficient code for ML workflows and data pipelines. Experience working with cloud platforms (e.g., AWS, GCP, Azure) for model deployment and compute orchestration. Solid software engineering fundamentals, including Git, modular design, and code testing. Practical experience with data preprocessing, feature engineering, and analysis of large datasets.

About Clarifai Clarifai is a leading, compute orchestration AI platform specializing in computer vision and generative AI. We empower organizations to transform unstructured image, video, text, and audio data into actionable insights, significantly faster and more accurately than manual processes. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been at the forefront of AI innovation since achieving the top five placements in the 2013 ImageNet Challenge. Our diverse, globally distributed team operates across the United States, Canada, Estonia, Argentina, and India. We have secured $100M in funding, including a $60M Series C round, backed by industry leaders such as Menlo Ventures, Union Square Ventures, Lux Capital, NEA, LDV Capital, Corazon Capital, Google Ventures, NVIDIA, Qualcomm, and Osage. Clarifai is proud to be an equal-opportunity workplace committed to building and maintaining a diverse and inclusive team. The Opportunity As a Senior Research Scientist at Clarifai, you'll contribute to applied research initiatives, converting the latest academic insights into production-ready solutions. You'll collaborate closely with our MLOps, Engineering, Business Development, and Product teams to rapidly prototype and deliver innovative capabilities, particularly within the national security domain. Your deep expertise in Computer Vision, GenAI, and multi-modal AI will drive strategic advancements and customer success. We seek individuals passionate about impactful AI applications, committed to collaboration, and skilled in managing multi-phase projects from initial proof-of-concept through deployment. Continuous learning and active participation in academic and industry forums are core elements of our research environment. Key Responsibilities Train, evaluate, and optimize machine learning models for high performance, scalability, and robustness. Contribute to R&D in object detection and multi-object tracking for remote sensing, including Synthetic Aperture Radar (SAR), and rapidly prototype proof-of-concept systems. Leverage and build AI data engines—scalable feedback systems that integrate model inference, human-guided labeling, and automated evaluation—to accelerate dataset growth and model refinement. Design and deliver production-grade, maintainable code while managing multi-phase development aligned to technical and customer objectives. Collaborate across teams and stakeholders—especially in national security and defense—to ensure effective knowledge transfer and mission-aligned innovation. Impact Your work as a Senior Research Scientist will significantly influence Clarifai's capability to deliver innovative AI solutions to the national security and intelligence communities. You will directly contribute to strategic projects that enhance Clarifai's reputation and position as a market leader in AI-driven geospatial analysis. Requirements 3+ years of hands-on experience developing neural networks, focusing particularly on Computer Vision and/or GenAI. Expertise in Python, with strong proficiency in libraries such as PyTorch, TensorFlow, or Jax. Advanced degree (Master's or PhD) in Computer Science, Mathematics, Engineering, or related fields. Great to Have Experience working with government, defense, or intelligence community R&D projects. Familiarity with remote sensing data sources, including commercial satellite imagery, UAS video, and NTM. Experience with LLMs, RAG, PEFT, and multi-modal applications (e.g., Captioning, VQA, cross-modal retrieval). Familiarity with the Model Context Protocol (MCP) and its use in structured agent communication, task orchestration, and context management across multi-agent systems. Published research in Computer Vision, NLP, or multi-modal AI. PhD in Machine Learning or related disciplines.

About the Company: Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision, natural language processing, and audio recognition. We help organizations transform unstructured images, video, text, and audio data into structured data at a significantly faster and more accurate rate than humans would be able to do on their own. Founded in 2013 by Matt Zeiler, Ph.D. Clarifai has been a market leader in AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai continues to grow with employees remotely based throughout the United States and in Tallinn, Estonia. We have raised $100M in funding to date, with $60M coming from our most recent Series C, and are backed by industry leaders like Menlo Ventures, Union Square Ventures, Lux Capital, New Enterprise Associates, LDV Capital, Corazon Capital, Google Ventures, NVIDIA, Qualcomm and Osage. Clarifai is proud to be an equal opportunity workplace dedicated to pursuing, hiring, and retaining a diverse workforce. The Opportunity: As a Senior Engineer, you build the systems and services behind the Clarifai magic. You will focus on the development of the model workflow engine and of Retrieval Augmented Generation (RAG) systems. Impact: You build the systems and services that will power some of Clarifai's newest offerings. They will enable customers to perform automated tasks and synthesise internal information using LLMs and other models. Requirements: Minimum of 6 years of backend software development experience required. Proficiency in one or more object-oriented programming languages and relational database management systems. Ability to manage multiple projects simultaneously is highly valued at Clarifai. Thrives in a fast-paced work environment. Experience working on distributed teams is preferred, with strong communication skills and transparency being key. Enjoys mentoring junior engineers and interns. Familiarity with Agile methodologies is a plus. Great to Have: Experience with GO or Python ML related experience Experience with Kubernetes

About Clarifai Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision, natural language processing, and audio recognition. We help organizations transform unstructured images, video, text, and audio data into structured data at a significantly faster and more accurate rate than humans would be able to do on their own. Founded in 2013 by Matt Zeiler, Ph.D. Clarifai has been a market leader in AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai continues to grow with employees remotely based throughout the United States and in Tallinn, Estonia. We have raised $100M in funding to date, with $60M coming from our most recent Series C, and are backed by industry leaders like Menlo Ventures, Union Square Ventures, Lux Capital, New Enterprise Associates, LDV Capital, Corazon Capital, Google Ventures, NVIDIA, Qualcomm and Osage. Clarifai is proud to be an equal opportunity workplace dedicated to pursuing, hiring, and retaining a diverse workforce. Impact We believe that world-class AI is built on a foundation of world-class data. The AI Data Lead for will own the critical, end-to-end process of creating and curating the high-quality datasets that fuel our models. You will be a power user of Clarifai's suite of automated data labeling products, providing direct feedback to our product and engineering teams to drive continuous improvement. Initially, this role will concentrate on building our next-generation vision datasets, with a heavy emphasis on full-motion video. Over time, the scope will strategically expand to include the development of our large-scale language datasets for advanced NLP models. Opportunity Dataset Strategy & Pipeline Development: Collaborate with ML and product teams to define data requirements, starting with complex video and image use cases and expanding into text and language. Design and execute a comprehensive strategy for data acquisition and augmentation. Build, scale, and maintain robust data pipelines to ingest, process, and version large-scale multimedia datasets. Third-Party Labeling & Internal Tool Management (Primary Focus): Leverage Clarifai's automated and AI-assisted labeling tools to efficiently pre-label data and manage human-in-the-loop workflows. Serve as the primary lead for external data labeling vendors who will often verify or enrich AI-generated labels, ensuring projects are on time and within budget. Author crystal-clear labeling instructions for complex tasks, from object tracking in video to, eventually, named entity recognition in text. Implement and manage a rigorous quality assurance (QA) framework for both AI- and human-generated labels. Product Feedback & Improvement Loop: Act as a key internal customer for Clarifai's data labeling products. Provide structured, expert feedback to our product and engineering teams to identify bugs, suggest feature enhancements, and guide the product roadmap. Continuously evaluate and pioneer new strategies for combining automated labeling with human verification to maximize quality and efficiency. Leadership & Collaboration: Lead and mentor a focused set of data labeling partners. Foster a culture of data excellence, ownership, and continuous improvement. Communicate project status, challenges, and outcomes effectively to all stakeholders. Keep track of budgets. Requirements 3+ years in data engineering, with a proven history of building and managing complex data pipelines. Direct, hands-on experience managing third-party data labeling services or in-house annotation teams. Experience working with large-scale vision datasets (image or video). Deep understanding of data labeling processes and quality metrics. Strong proficiency in Python and SQL. Experience with cloud data services (AWS, GCP, or Azure). Exceptional project management, communication, and vendor management skills. A meticulous eye for detail and an unwavering commitment to data quality. Great to Have Specific experience with the complexities of full-motion video datasets and annotation (e.g., temporal consistency, event tagging). Experience in an environment where you regularly used internal tools and provided feedback for their improvement ("dogfooding"). Experience with large-scale language or text datasets. Previous experience in a technical leadership or mentorship role. Experience using a variety of data annotation platforms and tools.

About The Company Clarifai is a leading, compute orchestration AI platform specializing in computer vision and generative AI. We empower organizations to transform unstructured image, video, text, and audio data into actionable insights, significantly faster and more accurately than manual processes. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been at the forefront of AI innovation since achieving the top five placements in the 2013 ImageNet Challenge. Our diverse, globally distributed team operates across the United States, Canada, Estonia, Argentina, and India. We have secured $100M in funding, including a $60M Series C round, backed by industry leaders such as Menlo Ventures, Union Square Ventures, Lux Capital, NEA, LDV Capital, Corazon Capital, Google Ventures, NVIDIA, Qualcomm, and Osage. Clarifai is proud to be an equal-opportunity workplace committed to building and maintaining a diverse and inclusive team. Key Responsibilities Identify trending open-source AI models with strong community adoption, import them into the Clarifai Community, and validate them across real-world use cases. Create clear, engaging previews and demos—both technical and non-technical—that showcase model capabilities. Collaborate with Marketing to promote new models and generate compelling content around them. Engage with the open-source AI community to build relationships with original model authors and increase backlink visibility. Develop lightweight Python-based demos and utilities to highlight model performance and usability. Impact As an ML Community Ops Engineer, you will directly contribute to growing Clarifai's model ecosystem by adding cutting-edge AI models and making them accessible to users. Your work will expand Clarifai's reach, improve discoverability, and ensure our platform remains at the forefront of open-source AI. By bridging engineering, marketing, and community engagement, you'll help solidify Clarifai's presence in the AI developer ecosystem. Requirements Strong experience developing, fine-tuning, and evaluating machine learning models, including familiarity with model architectures and key evaluation metrics. Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and architectures such as transformers and CNNs. Actively follows AI and ML trends—staying current with emerging models, benchmarks, and communities. Proficiency in Python, with ability to write clean, efficient code for ML workflows and data pipelines. Experience working with cloud platforms (e.g., AWS, GCP, Azure) for model deployment and compute orchestration. Solid software engineering fundamentals, including Git, modular design, and code testing. Practical experience with data preprocessing, feature engineering, and analysis of large datasets. Great to Have Strong experience developing, fine-tuning, and evaluating machine learning models, including familiarity with model architectures and key evaluation metrics. Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and architectures such as transformers and CNNs. Actively follows AI and ML trends—staying current with emerging models, benchmarks, and communities. Proficiency in Python, with ability to write clean, efficient code for ML workflows and data pipelines. Solid software engineering fundamentals, including Git, modular design, and code testing. Practical experience with data preprocessing, feature engineering, and analysis of large datasets.