• Lead high-impact projects: Design and deliver end-to-end data science solutions that support product innovation and business strategy. • Uncover insights: Analyze large, complex datasets to identify trends, surface opportunities, and influence key decisions. • Build models: Develop and deploy predictive and prescriptive models using machine learning and statistical techniques. • Enable experimentation: Design A/B tests and causal inference studies to help teams learn quickly and make informed choices. • Collaborate cross-functionally: Work closely with product managers, engineers, and business leaders to understand goals and deliver data-driven solutions. • Promote data fluency: Build dashboards, tools, and frameworks to enable self-service analytics and scale your impact across teams.

Background The Common Alerting Protocol (CAP) is the international standard format for emergency alerting, designed to deliver critical information across all media, for all types of hazards, to anyone at risk. CAP plays a vital role in strengthening coordination among organizations and improving the timely dissemination of alerts, thereby enhancing the effectiveness of multi-hazard early warning systems, and Cg-Ext (2025) passed Technical Regulations that states: “Designated official provider(s) of early warning services should routinely utilize the Common Alerting Protocol (CAP) of the International Telecommunication Union (ITU) for the dissemination of early warning service information and derived products for meteorological, hydrological, climatological, and related environmental hazards”. In support of the urge from Congress to implement the Technical Regulations, the WMO Early Warning Services (EWS) Section is actively promoting the adoption and long-term sustainability of CAP across all WMO Members. As part of this effort, WMO has developed an updated CAP e-Learning Course hosted on the WMO Division of Capacity Development (CD) Moodle platform (Course: Common Alerting Protocol e-Course | CD Moodle Site). Responding to requests from Members to make such learning resources available in additional languages, WMO is now embarking on a translation and implementation initiative. The course will initially be translated into Spanish, French and Arabic to better meet the needs of many developing and least developed countries, where there is an urgent demand for CAP implementation. To achieve this, the services of an Instructional Designer/Technologist are being sought. The consultant will play a key role in adapting the CAP e-Learning Course by deconstructing the current content for translation, coordinating with translators and reconstructing the course materials including text, graphics and media into multilingual versions. The consultant will also ensure that the translated and adapted courses are fully integrated and functional within the CD Learning Management System (Moodle), working closely with WMO focal points and the EWS Section. This consultancy will therefore directly contribute to strengthening capacity to understand, adopt and sustain the use of CAP in alerting and early warning systems. Duties and Responsibilities The Consultant will be responsible for finalizing the English version of the e-Learning Course and preparing materials for translation and implementing the CAP e-Learning Course into French, Spanish and Arabic, ensuring full functionality and fidelity to the original English version hosted in Moodle. More specifically, the Consultant will be required to: 1. Inception and Coordination Attend an Inception Meeting with the WMO Secretariat focal points and key members of the WMO Expert Teams to review the project scope, deliverables, timelines, and communication arrangements. 2. Course Review and Preparation Review the existing English version of the CAP e-Learning Course and implement any required revisions to correct formatting or typographical errors identified prior to translation. 3. Content Extraction for Translation - Export all required text and graphics from the Shareable Content Object Reference Model (SCORM) packages and the Moodle-based course pages hosted on the WMO CD Moodle platform. - Ensure extracted content is well structured and formatted for professional translation (for example in editable text or tabular formats). - Document file organization and maintain consistent naming conventions to facilitate efficient translation and reintegration. 4. Video and Media Preparation - Extract all required titles, critical graphics and narration transcript from videos for translation. - Prepare captions or subtitle files (for example SRT or VTT) for translation, ensuring time codes are preserved. 5. Integration of Translated Content – Course Materials - Reintegrate translated text and graphics into SCORM packages and Moodle pages for the French, Spanish and Arabic versions. - Adjust layouts, fonts, and navigation as needed to accommodate differences in language length and formatting. - Ensure functional integrity of interactivity, quizzes, and navigation across all versions. 6. Integration of Translated Content – Videos - Reintegrate translated titles and critical graphics into video materials. - Create and synchronize new caption files using translated text for each target language. - Verify video quality and synchronization accuracy following reintegration. 7. Development of Print Versions - Produce a “Print Version” of each translated course (revised English, French, Spanish and Arabic) containing views of every page of the course. - Include any hidden or scrollable text in a notes section to ensure full textual coverage. 8. Reconstruction of Moodle-based Assessment - Export all Moodle-based assessments for translation. - Recreate the translated versions in Moodle, verifying correct question formatting, feedback and functionality. 9. Coordination and Quality Assurance - Collaborate with focal points and translators to clarify technical or linguistic issues and ensure the accuracy of translated content. - Participate in periodic coordination meetings to review progress and address implementation questions. - Support quality assurance processes, including possible external reviews by native speakers, acknowledging that these reviews may influence the project timeline. It is acknowledged that the work of the Consultant will be closely related to the speed of translations and collaboration with translators for the Spanish, French and Arabic versions of the course. The Consultant will deliver all outputs in accordance with the agreed project schedule, ensuring that all translated versions of the CAP e-Learning course are complete, functional and of high quality. It is expected that the successful candidate will have access to their own licensed copy of Articulate Storyline (or Articulate 360). Required Skills and Experience Education: The minimum level of qualification and experience required by the consultant include: - A Bachelors (BSc.) or masters (MSc.) degree in Educational Technology, Instructional Design or a related field. Equivalent professional experience may substitute for formal education. Experience: Five years of proven experience designing, implementing, or localizing e-learning courses within an LMS, ideally Moodle - Demonstrated ability to manage SCORM-based content, including exporting, editing, packaging, and reimporting modules. - Desired experience preparing e-learning materials for translation/localization workflows (e.g., exporting text for translation, maintaining consistent structure, reintegrating translated materials). Knowledge and Skills: Working knowledge of e-learning authoring tools, in particular, experience using Articulate and Storyline. Languages: Fluency in oral and written English is required. Fluency in other WMO languages including French, Spanish and Arabic would be an asset. Salary and Allowances: Pay band C - USD 390 - 560 Duration: 30 days over a period of 6 months Focal Point: Adanna Robertson-Quimby arobertson@wmo.int Applications: Applications should be made online through the WMO e-recruitment system. Do not send your application via multiple routes. WMO no longer accepts applications via post or email. Only applicants for whom WMO has a further interest will be contacted. Shortlisted candidates may be required to sit a written test and/or an interview. Statements: WMO is committed to achieving diversity and a balanced workforce. Applications are welcome from qualified women and men, including those with disabilities. The statutory retirement age is 65. Pursuant to WMO Standing Instructions, the minimum age to be eligible for consideration for vacant positions is 18, and the maximum age must enable the candidate to serve for at least the term of the contract before reaching mandatory age of separation. Sexual harassment, exploitation, and abuse of authority WMO does not tolerate harassment, sexual harassment, exploitation, discrimination and abuse of authority. All selected candidates, therefore, undergo relevant checks and are expected to adhere to the respective standards and principles. Scam alert WMO does not charge a processing fee at any stage of its recruitment, selection, and hiring processes (i.e., application stage, interview stage, validation stage, or appointment and training). WMO will not ask for applicants’ bank account information.

Role Description This engagement is focused on building an internal AI platform that enables developers to ship AI-powered services efficiently. The objective is to improve DevEx and reduce time-to-market for AI features. Location: Serbia (relocation support available), Croatia, Poland, Portugal - Build and operate the AI platform infrastructure enabling developers to ship LLM-based services faster. - Implement and maintain Kubernetes-based runtime environments (incl. AKS) for AI workloads. - Manage infrastructure as code with Terraform (modules, environments, CI/CD automation). - Support LLM workflows: RAG, agents, prompt experimentation, evaluations, and deployment patterns. - Integrate and operate tooling such as Azure AI Foundry, LiteLLM, Langfuse, MLflow. - Orchestrate pipelines using Kubeflow Pipelines and/or Argo Workflows (build, deploy, evaluate). - Improve platform reliability and observability (monitoring, logging, tracing, cost/perf signals). - Collaborate closely with developers to streamline DX (APIs, templates, docs, golden paths, automation). Qualifications - Strong hands-on experience with Kubernetes in production (preferably AKS). - Solid Terraform expertise (IaC best practices, multi-env setups). - Practical experience supporting ML/LLM workloads in a platform or DevOps/MLOps context. - Proficiency in Python for automation, scripting, and supporting APIs/evaluation tooling. - Understanding of CI/CD, release processes, and production-grade operations. - Ability to work under tight timelines and deliver pragmatically. Requirements - Experience building internal developer platforms or “paved roads” for engineering teams. - Familiarity with LLM evaluation frameworks, prompt testing workflows, and LLM observability. - Exposure to RAG architectures, vector databases, and agentic patterns. - Experience with Kubeflow, Argo, and ML lifecycle tooling. Benefits - Long-term B2B contract. - Join a team of 5, with 3 AI Platform Engineers being added. - Remote work from Croatia, Poland, Portugal, and Serbia. - European working hours. - Occasionally available for meetings up to 10:00 AM PST (US overlap).

GoodAtNumbers is building an always-on decision intelligence platform that is trying to replace what a data scientist, data analyst and a business analyst does. We are looking for someone who can help us push both the research quality and production quality of our ML systems forward. We are hiring a Machine Learning Engineer Intern for a paid 12-week summer internship from May through July 2026. This is a remote role based in the United States and is expected to be 40 hours per week. Compensation for this internship is $30/hour. This role sits at the intersection of ML research, software engineering, and MLOps. You will work on problems related to retrieval, context construction, model/tool orchestration, evaluation, monitoring, and the productionization of AI systems. This is a strong fit for someone who can move from experiments to production code and who wants to work on real product problems instead of isolated notebooks. What you’ll work on - Design and run experiments across areas such as retrieval, ranking, context construction, tool use, grounded generation, model evaluation, anomaly detection, forecasting, or optimization workflows - Improve the quality, reliability, latency, and observability of ML and LLM-driven features - Build reproducible evaluation workflows for model behavior, answer quality, grounding, failure analysis, and regression testing - Help productionize research work through pipelines, APIs, services, monitoring, versioning, and deployment workflows - Improve MLOps practices around experiment tracking, prompt/model versioning, dataset versioning, testing, rollout safety, and post-deployment monitoring - Collaborate closely with software and platform engineers to ship ML systems that are useful, measurable, and production-ready What success looks like by the end of the internship - At least one meaningful ML or LLM system is measurably improved in quality, reliability, or latency - Research work is backed by reproducible evaluation and monitoring rather than one-off experimentation - The path from experiment to production is cleaner, faster, and safer What we’re looking for - 3–4 years of relevant experience preferred through research labs, internships, startups, open-source work, or production ML systems - Strong software engineering ability and strong comfort writing production-quality code - Strong Python skills preferred - Experience with machine learning experimentation, evaluation, and debugging preferred - Experience with LLMs, retrieval systems, vector search, ranking, prompt/tool workflows, or agent-style systems preferred - Experience with MLOps practices such as experiment tracking, versioning, model testing, deployment, and monitoring preferred - Comfort with statistics, error analysis, benchmarking, and translating ambiguous research ideas into shippable systems - Strong communication and the ability to document tradeoffs, assumptions, and results Nice to have - Experience with PyTorch, Transformers, or modern ML tooling - Experience with vector databases, RAG systems, or evaluation harnesses - Experience with time-series forecasting, causal analysis, anomaly detection, or optimization systems - Experience with Docker, Kubernetes, cloud infrastructure, or batch/orchestration systems - Publications, benchmark work, or strong public repos/writeups Work authorization Applicants must be authorized to work in the United States for the full internship period and must be based in the U.S. during the internship. We are not able to provide employment visa sponsorship for this internship. We welcome applicants from all backgrounds and evaluate candidates based on technical depth, execution, communication, and fit for the role.