Role Description We are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The ideal candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines. Key Responsibilities - Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering - Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks - Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets - Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines - Work with structured and unstructured data at scale — including text, tabular, and time-series data - Monitor model performance in production and implement retraining and drift-detection strategies - Collaborate with engineering and product teams to translate data insights into actionable AI features - Document data schemas, model architectures, and pipeline logic clearly and thoroughly Qualifications - Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar) - Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets - Practical experience with model training, evaluation, hyperparameter tuning, and deployment - Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts - Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar) - Strong grasp of statistical concepts, data quality principles, and model performance metrics - Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered. Preferred Qualifications - Experience with distributed data processing frameworks (Spark, Dask, or similar) - Familiarity with vector databases and embedding-based retrieval systems - Background working with real-time or streaming data pipelines (Kafka, Flink, or similar) - Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) - Experience with data governance, lineage tracking, or compliance-aware data workflows Tools & Technology - Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy) - MLOps: MLflow, Weights & Biases, DVC, or equivalent - Data warehouses and lakes: Snowflake, BigQuery, Redshift, or similar - LLM platforms: Hugging Face, OpenAI, Anthropic, or similar - Cloud infrastructure: AWS, GCP, or Azure - Google Workspace, Slack, Zoom, and remote collaboration tools Please note: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps — the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles. We connect top talent with vetted employers, competitive pay, and real growth opportunities.

Role Description Provision and manage virtual machines across multiple environments. Perform OS configuration, patching, and basic hardening across Linux and Windows systems. - Monitor VM health including CPU utilization, memory usage, disk performance, and service availability. - Assist with backup configuration and validation procedures. - Support restore testing activities for virtual systems. - Troubleshoot VM performance issues and boot related incidents. - Maintain VM inventory and infrastructure documentation. - Assist with operational monitoring and alert investigation. - Support environments that follow federal security and compliance requirements. - Collaborate with cloud engineers and infrastructure teams during system changes. Qualifications - Need CompTIA Security+ certification Requirements - DoD 8570 / 8140 Security+ or equivalent - 7-10 years of experience supporting enterprise infrastructure systems or virtual platforms - Bachelor's Degree

• Desenvolver, adaptar, avaliar e operacionalizar modelos e componentes de Machine Learning. • Trabalhar com embeddings, rerankers, classificadores, modelos de linguagem, modelos especializados e mecanismos de avaliação. • Criar pipelines para experimentação, validação, versionamento, deploy e monitoramento de modelos. • Implementar práticas de MLOps e LLMOps, conectando modelos ao ciclo de vida produtivo da plataforma. • Avaliar desempenho, custo, latência, qualidade, segurança e estabilidade de modelos em diferentes cenários. • Apoiar estratégias de fine tuning, prompt evaluation, dataset curation, model routing, fallback e otimização de inferência. • Construir métricas, testes e processos para garantir qualidade e confiabilidade dos modelos em produção. • Trabalhar com Engenharia de IA, Dados e Plataforma para integrar modelos a serviços, APIs e aplicações. • Documentar experimentos, decisões técnicas, critérios de avaliação e padrões reutilizáveis.

Role Description The E-learning Developer supports Orbis’s goal of ending avoidable blindness by creating e-learning content for health care professionals to improve their knowledge and skills to detect, diagnose, and treat blinding eye conditions. The E-learning Developer is responsible for the hands-on technical construction, programming, and maintenance of multi-language online courses for eye health professionals delivered through Orbis’s Cybersight platform. Cybersight’s Learning Management System currently has over 140,000 users and a collection of 150+ custom online courses. The successful candidate must be able to use adult education/instructional design theories and appropriate technologies to create engaging courses for eye professionals around the world, the majority of whom are in low- and middle-income settings. Qualifications - Diploma/Bachelor’s degree or equivalent experience in L&D, communications, adult education/instructional design, educational technology, or information and communications technologies. - A minimum of 3 years' experience in e-learning content creation using instructional design and adult learning principles. - Advanced-level proficiency in professional e-learning authoring platforms, with expert-level hands-on experience in Articulate Rise 360 and Articulate Storyline 360. Proficiency in iSpring and Articulate 360 platforms is highly valued. - Proven track record of developing and publishing interactive courses using industry-leading authoring tools, with expertise in SCORM and xAPI compliance for LMS integration. - A minimum of 3 years’ experience working with Learning Management System platforms, including end-user support. - Demonstrated expertise in developing and testing responsive, multi-device compatible courses (desktop, tablet, mobile) using advanced authoring tool features and rigorous QA practices. - Hands-on experience in using HTML5/CSS3, custom JavaScript in e-learning courses to enhance interactive elements, multimedia, etc. - Practical experience implementing SCORM 1.2 and 2004 standards, along with xAPI/Tin Can API for advanced learning analytics and LMS integration. - Experience working in medical education desirable. Requirements - Genuine interest in public health and “technology for good”. - Tech-savvy: comfortable diagnosing and solving technical problems (e.g. using Chrome Developer Tools, editing HTML and JavaScript). - Excellent interpersonal and communication skills (verbal and written); ability to interact and collaborate effectively with people of diverse cultural and professional backgrounds. - Strong organizational & planning skills with keen attention to detail: ability to manage multiple tasks and details efficiently while meeting deadlines. - Ability to work independently, with minimal supervision, and exercise independent judgement regarding operational decisions, processes, or systems. Benefits - Orbis is an Equal Opportunity Employer. - We are committed to providing equal employment opportunities to all employees and applicants, regardless of race, color, religion, gender, sexual orientation, disability, veteran status, or any other protected characteristic. - We encourage applications from qualified individuals of all backgrounds, cultures, and communities, particularly those underrepresented in the international nonprofit sector. - Orbis is proud to foster an environment where everyone can contribute meaningfully and thrive.