Join our Team Research Intern As the tech firm that created the mobile world, and with more than 60,000 patents to our name, we've made it our business to make a mark. When joining our team as an Intern at Ericsson you get a taste of shaping the future of technology. This is a place where you're welcomed as your own perfectly unique self, and celebrated for the skills, talent, and perspective you bring to the team. Are you in? Come, and be where it begins. Our Exciting Opportunity Ericsson Research develops new communication solutions and standards. The organization has a responsibility to provide Ericsson with world-class system concepts, technology innovations, and methodology. We are looking for an Intern to work together with our recently established research group in Japan. The focus is on air interface concepts, algorithm research, standardization (mainly 3GPP), and potentially some testbed/trial activities. What you will do & You will bring What you will do • Investigate and evaluate concepts and solutions for future radio access technologies and standards, related to 5G Evolution and 6G, • Develop and improve work of existing standards, • Work with physical layer techniques (multiple access technologies, modulation, channel coding, parameter estimation, multi-antenna transmitter and receiver algorithms, etc.), • Use simulation tools as means of evaluation. Depending on the achieved results during the internship, it will be possible to write publications. You will bring • B.Sc. or M.Sc. degree, preferably with demonstrated experience and knowledge in the above-mentioned required skills. • Competence and experience in signal processing, wireless communications, and machine learning. • Programming skills, for example, python, Java, MATLAB, C++, because computer simulations will be natural parts of the work in addition to analytical evaluations. • Good knowledge in simulation and evaluation tool methodology. • Good communication in written and spoken English is needed. Good communication in Japanese is preferable. • Ability to handle theoretical and practical research as well as the development aspects of the work. • Team skills, since collaboration and interaction with other skilled researchers daily, are the basis for our ways of working. Why join Ericsson?At Ericsson, you'll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what's possible. To build solutions never seen before to some of the world's toughest problems. You'll be challenged, but you won't be alone. You'll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next. What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more. Primary country and city: Japan (JP) || Yokohama Req ID: 784213

Role Description At Apheris, we power federated data networks in life sciences to address the data bottleneck in training highly performant ML models. Our product enables biopharma organizations to collaboratively train higher quality models on their combined data. The Apheris product is a set of drug discovery applications enriched with the proprietary data of network participants. Our federated computing infrastructure with built-in governance and privacy controls ensures that the data IP and ownership always stays with the data custodians. We are looking for a Senior ML Engineer to drive the technical execution for our structural biology models. This is a hands-on, high-impact role focused on: - Advancing the state of the art in applying foundational models to structural biology problems. - Working closely with our leadership team as the technical authority on ML modelling, architecture, and experimentation. What you will do - Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2, and ESMFold. - Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines. - Work with customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data. - Carry out case studies associated with the above, providing scientific and technical expertise to customers. - Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production. - Collaborate cross-functionally to ensure models address real-world drug discovery needs. - Contribute to publications or open-source contributions where relevant. What we expect from you - By month 3: Develop a deep technical understanding of the Apheris product and contribute to delivery of at least one customer-driven cofolding project. - By month 6: Build a customer-ready package for results analysis and drive adoption of Apheris-generated models with customers. - By month 12: Take ownership of a customer-driven cofolding model development stream and drive product requirements. You should apply if - You have deep experience building and training contemporary models in production, at scale (e.g., AlphaFold, OpenFold, Boltz) and are familiar with modern MLOps tooling. - You have experience applying ML to real-world protein structure or drug discovery problems. - You are comfortable working in a fast-paced startup environment and enjoy customer-driven projects. - You understand the technical challenges of structural biology and can design scalable data preprocessing, training, and evaluation workflows. Nice to have - You have experience in federated learning, privacy-preserving ML, or privacy-preserving model training. - You’ve published in ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics). Benefits - Industry-competitive compensation, including early-stage virtual share options. - Remote-first working – work where you work best, whether from home or a co-working space near you. - Great suite of benefits, including a wellbeing budget, mental health benefits, a work-from-home budget, a co-working stipend, and a learning and development budget. - Regular team lunches and social events. - Generous holiday allowance. - Quarterly All Hands meet-up at our Berlin HQ or a different European location. - A fun, diverse team of mission-driven individuals with a drive to see AI and ML used for good. - Plenty of room to grow personally and professionally and shape your own role. Logistics Our interview process is split into three phases: - Initial Screening: If your application matches our requirements, we invite you to an initial video call to explore the fit. - Deep Dive: A domain expert will assess your skills and knowledge required for the role. - Final Interview: Up to three hours of targeted sessions with our founders, discussing our culture and meeting future co-workers.

Role Description - Optimize ML model serving for low-latency inference (target: sub-200ms P95) on EKS - Advise on and implement AWS-native ML infrastructure (SageMaker endpoints, model registry, A/B testing, monitoring) - Support ML-optimized rule weight calibration — training logistic regression / LightGBM on rule-fire indicators to learn optimal rule weights from labeled data - Assist with model retraining pipeline automation and drift detection - Contribute to model explainability documentation (SHAP-based attribution) for regulatory compliance - Participate in model governance: version control, audit trails, threshold configuration per participating institution - Support load testing and performance benchmarking of the ML scoring pipeline - Provide input for the technical proposal and architecture documentation Qualifications - AWS Machine Learning Specialty Certification (or AWS Certified Machine Learning Engineer – Associate) — current and valid - 3+ years of hands-on experience deploying ML models in production on AWS - Strong Python skills (scikit-learn, LightGBM/XGBoost, pandas) - Experience with containerized ML serving (Docker, Kubernetes/EKS) - Familiarity with model monitoring, drift detection, and retraining pipelines Requirements - Experience in fraud detection, AML, or financial risk systems - Familiarity with graph-based ML (GNN, NetworkX) for network analysis - Experience with Apache Kafka or Apache Flink for streaming ML - Knowledge of SHAP or other model explainability frameworks - Experience with SageMaker (endpoints, model registry, pipelines) Benefits - Fully Remote - Flexible working hours (part-time, ~15–20 hours/week) - Potential to extend engagement based on project phase progression

Company Description It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today - ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone. Job Description What you get to do in this role: PLATO (Platform Engineering and AI Technology Organization) at ServiceNow is a customer-focused innovative group building intelligent software using a variety of technology stacks to enable end-to-end, industry-leading work experiences for our customers. We are a group of people deeply invested in the success of our customers that happen to have expertise and knowledge in advanced technologies and software engineering best practices. We are data driven, structured, committed and we enjoy what we are doing. We prioritize robustness, performance and user experience over the technology stack and tools. We are a group of technology professionals and platform engineers with a dual mission. We build and evolve the AI platform, and partner with teams to build products and end-to-end AI-powered work experiences. In equal measure, we lay the foundations, research, experiment, and de-risk AI technologies that unlock new work experiences in the future. As a Staff Machine Learning Engineer - VoIP Infrastructure you will: - Contribute to the design, development and implementation of VoIP infrastructure, telephony platforms, and observability features that power AI-driven voice workloads - Collaborate with engineering, Product, and infrastructure teams to ensure our voice and AI platforms perform efficiently, scale reliably, and integrate seamlessly across SIP/RTP, Kamailio, RTPEngine, and related telecom systems. - Contribute to the continuous improvement of the SRE practice by turning operational telephony and AI workload use cases into requirements for software tooling. - Contribute to the execution of deployment and support activities for VoIP systems and AI/ML developers operating in production voice environments. - Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.). - Work with product owners to understand detailed requirements and own your code from design, implementation, test automation, and delivery - spanning both telephony infrastructure and LLM integration layers. - Experience integrating LLMs into voice platforms and real-time communication systems. - Be a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines. Qualifications To be successful in this role you have: - Hands-on experience building VoIP systems using SIP/RTP protocols; - Practical knowledge of Kamailio, RTPEngine, FreeSWITCH, SBCs, and PSTN systems (or similar); - Working knowledge of PSTN infrastructure and telecom protocols; - Experience integrating applications on top of LLMs (using existing models, not building them); - Experience in prompt engineering and developing LLM based features - 4+ years of development experience with Python, GoLang, Java or similar languages. - 4+ years of experience operating highly available distributed workloads on Kubernetes following a DevOps approach. - Working experience building distributed systems with cloud-native software; - Experience with software-defined networking, infrastructure as code and configuration management; - Experience with DevOps tooling (e.g. Helm / Ansible / Kubernetes / Prometheus /Splunk/ GitLab CI) is considered an asset - Experience building software for compliance and security in regulated environments is considered an asset - 4+ years of experience with infrastructure and platform operations, deployments, SRE, and DevOps with a continued focus on improving Platform health is considered an asset - Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. - Experience in using AI productivity tools such as Cursor, Windsurf, etc For positions in this location, we offer a base pay of $173,100 - $303,000, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Additional Information Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. 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