Career Category Information Systems Job Description Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Senior Machine Learning Engineer - Forecasting What You Will Do We are seeking a Senior Machine Learning Engineer, Forecasting to join the Forecasting team within the AI & Data organization. This role will design, build, deploy, and maintain scalable machine learning systems that power forecasting capabilities and uncertainty-aware decision support across the company. This senior member of the team will work cross-functionally to translate advanced forecasting methods into reliable, production-grade solutions that support critical business processes and help Amgen deliver on its “every patient, every time” mandate. The role is particularly well suited to a strong engineer who is excited about building robust ML infrastructure, productionizing state-of-the-art forecasting models, and enabling decision-support solutions that inform multi-horizon planning and business decision-making. Key Responsibilities - Design, build, and maintain scalable machine learning systems and forecasting pipelines to support demand forecasting across near-, medium-, and long-term planning horizons. - Productionize advanced statistical, Bayesian, and machine learning forecasting models, including training, validation, deployment, and lifecycle management. - Build and optimize data pipelines, feature engineering workflows, and batch and real-time inference systems using large, complex datasets. - Own the end-to-end ML engineering lifecycle, including solution design, prototyping, model integration, testing, deployment, monitoring, observability, and continuous improvement. - Develop robust MLOps capabilities, including model versioning, CI/CD, automated retraining, performance monitoring, drift detection, and rollback strategies. - Partner closely with data scientists and business stakeholders to operationalize forecasting, simulation, and scenario-analysis capabilities that support strategic decision-making. - Establish and promote software engineering best practices, including code quality, documentation, reproducibility, and system reliability. - Research and evaluate emerging tools, platforms, and methodologies in machine learning engineering, forecasting, and AI for potential application to business problems. Basic Qualifications - Doctorate degree OR - Master’s degree and 2 years of applying data science in enterprise environments experience OR - Bachelor’s degree and 4 years of applying data science in enterprise environments experience OR - Associate’s degree and 8 years of applying data science in enterprise environments experience OR - High school diploma / GED and 10 years of applying data science in enterprise environments experience Preferred Qualifications - 6+ years of experience in machine learning engineering, software engineering, or a related field, with a demonstrated track record of deploying production ML systems that deliver business value. - Strong experience building and maintaining end-to-end ML pipelines and production systems for forecasting or other predictive modeling use cases. - Expertise in model serving, and operationalizing probabilistic, Bayesian, or predictive models in production environments. - Strong programming skills in Python and SQL, with experience using tools such as scikit-learn, PyTorch, TensorFlow, and orchestration or workflow tools for ML pipelines. - Experience with cloud platforms, distributed data processing, containerization, and ML deployment patterns. - Strong understanding of software engineering fundamentals, including system design, testing, performance optimization, and maintainability. - Strong collaboration and communication skills, with the ability to work effectively across technical and non-technical teams. - An intellectually curious self-starter who can take ambiguous problems and build scalable solutions from the ground up. - Experience building and deploying forecasting models for biotech/pharma use cases with knowledge of healthcare commercial concepts such as payer/provider dynamics, formulary access, and coverage. - Experience partnering closely with data scientists to translate advanced statistical or machine learning models into reliable production services. - Experience leveraging machine learning and forecasting systems in retail, consumer goods, supply chain, or manufacturing applications. - Familiarity with model monitoring, explainability, and governance requirements in regulated or high-impact business environments. What You Can Expect Of Us As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way. The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications. In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include: - A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts - A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan - Stock-based long-term incentives - Award-winning time-off plans - Flexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies. Apply now and make a lasting impact with the Amgen team. careers.amgen.com In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information. Application deadline Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position. Sponsorship Sponsorship for this role is not guaranteed. As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease. Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. .Salary Range 156,190.05USD -211,315.95 USD

• Spearhead the end-to-end development, deployment, and stewardship of machine-learning solutions • Collaborate with Risk, Product, Operational and other teams • Translate business goals into robust models • Ensure ongoing performance of models • Identify new AI/ML opportunities that raise the company’s bottom line • Design, train, and deploy probability of default models • Build credit-limit strategies • Discover and scope AI/ML opportunities that boost efficiency and revenue • Produce and update internal model documentation • Implement model monitoring • Plan and execute A/B tests • Build Computer Vision pipelines • Develop LLM-based solutions to enhance customer experience

Tiered Approach In line with the commitment to safeguard capacity and support personnel already in the Organization, a majority of UNDP UNCDF/UNV vacancies are advertised using a tiered application process whereby: - Tier 0: UNDP/UNCDF/UNV IP staff holding permanent (PA) and fixed-term (FTA) appointments, whose posts will be abolished, or contracts will be terminated or not renewed during 2026. - Tier 1: Other UNDP/UNCDF/UNV staff holding permanent (PA) and fixed-term (FTA) appointments - Tier 2: UNDP/UNCDF/UNV staff holding temporary appointments (TA), personnel on regular PSA contracts, and Expert and Specialist UN Volunteers - Tier 3 or no tier indicated: All other contract types from UNDP/UNCDF/UNV and other agencies, and other external candidates Please make note of the Tier(s) indicated in the vacancy title, if any, and ensure that you satisfy the eligibility to apply. Background UNDP supports countries and communities to strengthen resilience to climate change through innovative, inclusive, and locally led adaptation solutions. In line with this mandate, UNDP implements and supports a diverse portfolio of climate adaptation initiatives that promote innovation, knowledge exchange, community leadership, and scalable approaches to resilience-building. Within this context, the Innovation for Adaptation – Learning and Knowledge Management Coordinator will support projects under the Adaptation Innovation Marketplace (AIM). Launched in 2021, AIM is a global platform designed to accelerate locally led adaptation innovations by addressing key barriers faced by local actors, including limited access to finance, technical support, and visibility. Currently AIM has anchored under its umbrella four projects: - UNDP-Adaptation Fund Climate Innovation Accelerator Phase 1 (closed). - Resilience for Peace and Stability, Food and Water Security Innovation Grant Programme (closed) - Balkan Climate Adaptation Futures: A Regional Innovation Initiative for Resilience (ongoing). - UNDP-Adaptation Fund Climate Innovation Accelerator Phase 2 (pipeline). Through its multi-partner network, AIM provides structured mechanisms to identify and support promising adaptation solutions, deliver tailored technical and enterprise development assistance, facilitate peer-to-peer learning and knowledge exchange, and connect innovators with potential investors and strategic partners. The initiative places a strong emphasis on strengthening learning processes and ensuring that knowledge generated through implementation is systematically captured, shared, and applied to improve results and support scaling. The Innovation for Adaptation – Learning and Knowledge Management Coordinator will work as part of a multidisciplinary team across country offices, regions, and thematic areas, collaborating closely with programme managers, monitoring and evaluation specialists, innovation teams, communications colleagues, and external partners. The role will play a central function in connecting grantees, partners, and stakeholders, supporting learning activities, and translating implementation experience into accessible and actionable knowledge products. Key responsibilities include fostering communities of practice, supporting capacity-building and peer-to-peer exchange, and contributing to strategic learning and knowledge management approaches that inform programme improvement, scaling, and resource mobilization. The position requires engagement with a wide range of stakeholders and sensitivity to diverse cultural contexts, inclusive approaches, and evolving programme needs. The Innovation for Adaptation – Learning and Knowledge Management Coordinator will ensure alignment with project objectives, Adaptation Fund requirements, and UNDP policies and standards, contributing to the effectiveness, coherence, and sustainability of AIM-supported interventions. The role will work in close collaboration with the Innovation for Adaptation Global Technical Specialist, the Istanbul Regional Hub, UNDP country offices, relevant Regional Bureau units, and Planet Hub teams to support coherent and timely implementation. UNDP is committed to achieving workforce diversity in terms of gender, nationality, and culture. Individuals from minority groups, indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the strictest confidence. UNDP does not tolerate sexual exploitation and abuse, any kind of harassment, including sexual harassment, and discrimination. All selected candidates will, therefore, undergo rigorous reference and background checks.  UNDP is the leading United Nations organization in fighting to end the injustice of poverty, inequality, and climate change. Working with our broad network of experts and partners in 170 countries, we help nations to build integrated, lasting solutions for people and planet. Learn more at undp.org or follow at @UNDP Duties and Responsibilities Under the overall guidance of the Innovation for Adaptation Global Technical Specialist, and in close coordination with the Regional Project Manager, the Innovation for Adaptation – Learning and Knowledge Management Coordinator will undertake the following functions: Provision of Learning, Knowledge Management Services and Development of Knowledge Products: - Contribute to the development and implementation of learning and knowledge management approaches to capture lessons, good practices, and innovations emerging from climate adaptation initiatives, including gender-responsive and socially inclusive approaches. - Identify, analyze, synthesize, and document key insights, lessons learned, and best practices linked to project goals and implementation. - Support the production of knowledge products, including stories, briefs, case studies, synthesis reports, guidance notes, and other learning materials. - Provide technical inputs, review, and quality assurance to ensure knowledge products are clear, accurate, consistent, and aligned with programme objectives, donor requirements, and UNDP communication standards. - Support the documentation of innovative practices, success stories, and lessons learned emerging from project implementation, including those highlighting women’s leadership, participation, and gender-responsive climate adaptation practices. - Coordinate with programme teams and partners to validate information and ensure the accuracy and completeness of documented results and knowledge materials. - Develop knowledge-sharing materials and respond to related knowledge requests as needed. Ensure Stakeholder Engagement, Partnerships and Knowledge Exchange: - Contribute tothe design and implementation of community engagement activities involving project stakeholders. - Facilitate peer-to-peer learning and knowledge exchange among grantees, partners, practitioners, and relevant networks. - Support the organization and delivery of learning sessions, trainings, workshops, webinars, and other knowledge-sharing events, ensuring balanced and inclusive participation of diverse stakeholders, including women and underrepresented groups. - Facilitate activities that strengthen stakeholder capacities in areas related to innovation, resilience, inclusive approaches, and scaling. - Promote and faciliatate collaboration with internal and external partners, including networks, knowledge platforms, and innovation actors, to strengthen knowledge exchange, visibility, and joint learning. - Assist in coordinating joint knowledge and learning activities with programme partners, ensuring alignment with programme objectives. - Encourage the sharing of experiences, tools, and lessons learned across projects, regions, and communities of practice. Provision of Technical Advisory and Strategic Programme Support: - Provide technical guidance and advisory support to project partners and grantees on learning, knowledge management, and implementation-related challenges, including capturing and communicating gender-responsive and inclusive results, in close coordination with project and M&E colleagues. - Contribute to strengthening partners’ capacity to capture, communicate, and utilize project results, lessons learned, and impacts. - Provide analytical and learning-based inputs to programme planning, strategic discussions, and decision-making processes. - Support the development and structuring of knowledge materials, including guidance notes, learning summaries, and thematic briefs, presentations by synthesizing key results, insights, and lessons learned. - Contribute learning-based inputs and evidence from project implementation to programme reporting, strategy development, and resource mobilization efforts. The incumbent performs other duties within their functional profile as deemed necessary for the efficient functioning of the Office and the Organisation Institutional Arrangement The Coordinator will work under the direct supervision of the Innovation for Adaptation Global Technical Advisor within the Climate Change Adaptation Team. The position will collaborate closely with the Regional Project Manager, IRH Digital and Innovation Team Leader, programme management teams, M&E specialists, innovation teams, communications colleagues, Country Offices, IRH, and relevant external and strategic partners. Competencies Core Competencies: - Achieve Results: LEVEL 3: Set and align challenging, achievable objectives for multiple projects, have lasting impact - Think Innovatively: LEVEL 3: Proactively mitigate potential risks, develop new ideas to solve complex problems - Learn Continuously: LEVEL 3: Create and act on opportunities to expand horizons, diversify experiences - Adapt with Agility: LEVEL 3: Proactively initiate and champion change, manage multiple competing demands - Act with Determination: LEVEL 3: Think beyond immediate task/barriers and take action to achieve greater results - Engage and Partner: LEVEL 3: Political savvy, navigate complex landscape, champion inter-agency collaboration - Enable Diversity and Inclusion: LEVEL 3: Appreciate benefits of diverse workforce and champion inclusivity Cross-Functional & Technical Competencies: Business Developement - Knowledge Generation: - Ability to research information and to turn it into useful knowledge, relevant for context, or responsive to a stated need. - Ability to apply existing concepts to new situations, and to develop new concepts to generate workable solutions and new approaches. - Knowledge of relevant concepts, conceptual models, and theories that can be useful in addressing new situations. Partnership management - Multi-stakeholder engagement and funding: - Knowledge and ability to forge multi-stakeholder partnerships, and remove any obstacles to resource mobilization and multi-stakeholder funding platforms 2030 Agenda: Engagement and Effectiveness - Innovation: - Innovation Ecosystem HR - Learning and development - Digital L&D: - Knowledge of digital learning methods and ability to design and develop digital learning programmes 2030 Agenda: Planet - Climate: - Climate Change Adaptation: Climate-Resilient Infrastructure Required Skills and Experience Education: - Advanced university degree (master’s degree or equivalent) in Environmental Studies, Climate Change, Development Studies, SocialSsciences, International Relations, International Law, Human Rights Law or a related field is required. - A first-level university degree (bachelor’s degree) in the areas stated above, in combination with an additional two years of qualifying experience will be given due consideration in lieu of the advanced university degree. Experience: - Minimum 5 years (with Master’s degree) or 7 years (with Bachelor’s degree) of relevant experience in areas such as community/stakeholder engagement, learning and knowledge management, programme/project design/implementation/ oversight, climate change, climate/environmental/biodiversity finance, innovation, or related areas within the context of sustainable development. Languages: - Fluency in English is required (written and spoken) Required Skills: - Experience in facilitating learning and knowledge exchange with communities of practice or with multi-stakeholder partnerships and platforms. This entails demonstrated experience coordinating events, workshops, webinars, mentorship programmes and trainings, peer-to-peer learning processes and exchanges, and preparing knowledge and learning guidance and materials. - Demonstrated experience in analytical work and strong writing, including the preparation of knowledge management products such as synthesis reports, learning briefs, factsheets, or similar. - Previous experience working with non-profit organizations (NGOs), civil society organizations (CSOs) and/or MSMEs that engage vulnerable groups is required; - Experience with international development projects implemented at the global and/or regional level is required. - Demonstrated experience supporting the design and implementation, monitoring, or knowledge management of Adaptation Fund–financed projects, including familiarity with its operational and reporting requirements. Desired Skills: - Experience in thematic areas involving entrepreneurial acceleration and/or entrepreneurial innovation is an asset. - Experience in the thematic areas of locally led adaptation and climate-related initiatives, will be considered an asset. - Demonstrated experience managing and engaging communities of practice via social media platforms, fundraising campaign websites, and other digital platforms is an asset. - Previous work experience with UNDP or similar international organizations is an advantage. Equal opportunity As an equal opportunity employer, UNDP values diversity as an expression of the multiplicity of nations and cultures where we operate and, as such, we encourage qualified applicants from all backgrounds to apply for roles in the organization. Our employment decisions are based on merit and suitability for the role, without discrimination. UNDP is also committed to creating an inclusive workplace where all personnel are empowered to contribute to our mission, are valued, can thrive, and benefit from career opportunities that are open to all. Sexual harassment, exploitation, and abuse of authority UNDP 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. Right to select multiple candidates UNDP reserves the right to select one or more candidates from this vacancy announcement. We may also retain applications and consider candidates applying to this post for other similar positions with UNDP at the same grade level and with similar job description, experience and educational requirements. Scam alert UNDP does not charge a fee at any stage of its recruitment process. For further information, please see www.undp.org/scam-alert. #LI-DNI

• Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent • Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models • Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers • Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance • Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health • Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments • Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities • Define engineering best practices and contribute to platform scalability in a fast-moving startup environment