AI/ML Engineer, Paris
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Madrid
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, London
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Berlin
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into the applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
IT Engineer
Collaborate directly with the GTM team including Account Executives and Solutions Architects to ensure smooth integration and successful deployment of machine learning solutions. Build and present compelling demonstrations and proof of concepts that showcase AI technology capabilities. Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs. Contribute to the internal machine learning platform by adding features and fixing bugs. Integrate and enable new machine learning models into the existing platform or client environments. Improve system performance, efficiency, and scalability of deployed models and applications. Work closely with partners to enable joint AI solutions and ensure seamless collaboration.
Machine Learning Engineer
Build and deploy AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading regular demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Set up telephony systems such as SIP, CCaaS, and PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Write and refine prompts for large language model-driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions, work consultatively to resolve challenges in security, connectivity, or knowledge ingestion. Collaborate with product and engineering teams to escalate platform gaps, resolve technical issues, and lead client implementations independently.
Manager of Technical Staff, Sovereign AI
As the Manager for the Sovereign AI Modelling team, you will manage a team of scientists and engineers, fostering a culture of high-performance, innovation, and continuous learning. You will stay up-to-date with the latest research in large language models (LLMs) and related fields, lead scalable strategies to train frontier models, and collaborate with cross-functional teams across modelling, forward-deployed engineering, and solutions architecture. The role is hands-on and research-driven, involving designing and implementing novel research ideas, shipping state-of-the-art models to production, and maintaining deep connections to academia and government. You will dive into the latest literature on LLMs, experiment with frontier models, and lead a team of talented engineers and researchers to build scalable, production-ready solutions.
Machine Learning Engineer (Semantic Scene Understanding)
Design and train state-of-the-art machine learning algorithms for semantic segmentation, object detection, and classification tailored to aerial imagery. Build high-level tactical features on top of base semantic data, such as real-time road vectorization, trafficability analysis, and dynamic obstacle mapping. Architect pipelines that temporally and spatially align semantic data from multiple moving UAVs into a cohesive Common Operational Picture (COP). Optimize and deploy these algorithms directly into the tactical C2 platform, utilizing quantization, pruning, and hardware acceleration to meet strict real-time compute constraints.
Machine Learning Engineer, Anonymization
Design, implement, and productionize advanced ML models and techniques such as federated learning, differential privacy, or synthetic data generation for data anonymization. Build and maintain the core backend infrastructure and APIs to securely process and serve anonymized data at Mercor's scale. Benchmark the anonymization pipeline against industry best practices and regulatory standards like k-anonymity, continuously running experiments to improve both privacy guarantees and data utility. Collaborate cross-functionally with Legal, Security, and Engineering teams to translate compliance requirements into robust, model-driven solutions. Act as the subject matter expert on data anonymization, balancing applied ML, complex data pipeline engineering, and driving architectural decisions for data privacy.
Machine Learning Intern (202641)
As a Machine Learning Intern at Nomagic, you will dive into complex problems of physical manipulation to enhance robot capabilities. Your responsibilities include expanding the perception abilities of the robotic system to handle a wider variety of products, detecting anomalies such as identifying when a robot picks more than one item or when an item is disassembling, training models to solve multiple problems with various loss functions, and productionizing machine learning models which involves performance monitoring and A/B testing. You will work on developing groundbreaking technology and collaborate with top professionals in an English-speaking environment, with opportunities to play with robots daily and contribute directly to impactful results.
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