Role Description As a Staff Applied Machine Learning Engineer focused on Fraud & Abuse, you will design, build, and operate production ML decision systems that reduce payment fraud, account takeover, identity abuse, merchant and marketplace risk, scams, and other adversarial activity across Block. The team optimizes for reliable decisions, safe deployment, and measurable customer outcomes — preserving access for good customers while reducing fraudulent, abusive, or unsafe activity. You should be comfortable owning production systems end to end: - Data contracts - Low-latency inference - Batch scoring - Feature quality - Online/offline consistency - Model deployment - Monitoring - Incident response - Rollback - Outcome feedback loops The work combines large-scale ML decisioning with AI-assisted operations: - Surfacing evidence - Simulating controls - Accelerating triage - Improving feedback loops while preserving human judgment in high-stakes decisions You will work closely with ML modelers, product engineers, risk analysts, compliance partners, and operations teams to respond quickly to evolving abuse patterns without creating unnecessary friction or harm for legitimate customers. Qualifications - 12+ years building and operating production software and ML systems for business-critical products - Deep expertise in fraud/risk domains such as payment fraud, identity/account integrity, merchant or marketplace risk, scams, trust & safety, abuse prevention, or compliance decisioning - Strong production ML judgment across feature pipelines, model serving, evaluation, monitoring, low-latency integration, safe rollout, and incident response - Sound judgment around false-positive tradeoffs, noisy labels, adversarial behavior, customer harm, and cross-functional decisions - Experience using AI-assisted engineering tools with appropriate verification, testing, and review for high-stakes systems Requirements - Experience with graph-based fraud detection, behavioral sequence models, embeddings, entity resolution, anomaly detection, or human-in-the-loop review - Experience building fraud operations tooling for triage, case management, alert clustering, graph exploration, or policy simulation - Experience with regulated financial services, model governance, auditability, explainability, or decision logging Technologies We Use and Teach - Python, Java, Kotlin, SQL - TensorFlow, PyTorch, XGBoost/LightGBM, embeddings, deep learning, and tree-based modeling ecosystems - Kafka or other event-streaming systems, batch data pipelines, feature stores, workflow orchestration, and model-serving systems - Cloud infrastructure, Kubernetes, data warehouses/lakehouses, monitoring, observability, coding agents, evaluation harnesses, and agent-assisted operations tooling Benefits - Remote work - Medical insurance - Flexible time off - Retirement savings plans - Modern family planning

• Build and deploy models to detect and flag suspicious behavior (classification, anomaly detection, clustering, ranking, and other approaches as appropriate) • Own ML pipelines end-to-end (feature generation, training, evaluation, batch/streaming inference, backfills, and versioning) • Design evaluation + monitoring: ground truth strategies with Threat Intel, offline metrics, drift monitoring, and alerting • Collaborate with product & engineering to integrate intelligence into user-facing workflows (e.g., review queues, and decision-support tooling) • Improve data quality and accessibility by working with data engineering patterns (schemas, lineage, reproducibility, and access controls) • Contribute to operational rigor: runbooks, incident response, and safe rollout practices for systems that impact customer trust

• Deliver end-to-end ML use cases (recommendation systems, safety algorithms, moderation models, etc.). • Design, train, evaluate, and deploy ML models where appropriate. • Balance speed of delivery with robustness and reliability. • Drive improvements across the ML lifecycle (training, deployment, monitoring, iteration). • Establish KPIs and monitoring standards to track model performance over time. • Ensure continuous alignment with product and safety objectives. • Help improve the reliability and observability of production ML systems. • Contribute to the evolution of our ML platform through tools, workflows, and reusable components. • Help establish and promote scalable ML engineering practices across teams. • Take ownership of legacy models and realign them with current business needs. • Improve, retrain, and integrate them into modern pipelines. • Explore and implement advanced ML approaches where relevant. • Partner with Data Engineering, MLOps, Backend Platform, and Product teams. • Act as a bridge between ML, platform, and business stakeholders.

Company Description Nexthink is the leader in digital employee experience management software. The company provides IT leaders with unprecedented insight allowing them to see, diagnose and fix issues at scale impacting employees anywhere, with any application or network, before employees notice the issue. As the first solution to allow IT to progress from reactive problem solving to proactive optimization, Nexthink enables its more than 1,300 customers to provide better digital experiences to more than 18 million employees. Dual headquartered in Lausanne, Switzerland and Boston, Massachusetts, Nexthink has 9 offices worldwide. #LI-Hybrid Job Description Are you passionate about AI and eager to drive innovation in a fast-paced, impact-driven environment? Do you have experience developing AI-powered applications and enjoy mentoring others? If so, we invite you to join Nexthink as a Senior ML Engineer! As a key member of our AI team, you will design, develop, and deploy scalable AI solutions, leveraging cutting-edge technologies to enhance digital employee experiences for millions worldwide. Responsibilities - Lead AI Development - Design and implement AI-powered products, with a strong focus on Agentic systems. - LLMs done right - Create evaluations and define metrics of success using cutting edge AI platforms. - MLOps - Implement and manage MLOps practices to streamline the deployment, monitoring, and maintenance of models and in production environments. - Collaboration & Leadership - Work closely with software engineers, data scientists, and product teams to align AI capabilities with business needs. - Mentorship & Coaching - Guide and support junior AI engineers, fostering a culture of learning and knowledge sharing. Qualifications - Bsc/Master's degree in Computer Science, Machine Learning, Data Science, or a related field. - 5+ years of hands-on experience in LLM-powered production applications. - Strong proficiency in Python and AI frameworks (e.g., LangChain, PyTorch). - Proven experience with AWS and cloud-based AI deployments. - Solid understanding of AI model fine-tuning, optimization, and evaluation. - Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment. - Eagerness to learn and adapt - Strong communication skills in English, capable of explaining complex AI concepts to technical and non-technical stakeholders - we believe in learning by sharing - Knowledge of reinforcement learning, retrieval-augmented generation (RAG), and multi-agent AI architectures. - Strong data intuition: ability to inspect raw logs, design metrics and quickly identify quality regressions - Hands-on experience with MLOps: reproducible pipelines, automated evaluations, feature stores, CI/CD for models or prompt/embedding configs - Familiarity with MLOps best practices for deploying and monitoring AI models in production. If you're excited about pushing the boundaries of AI and mentoring the next generation of engineers, we'd love to hear from you! Even if you don't meet every requirement, we encourage you to apply-we value expertise, passion, and the drive to learn. Additional Information We are the pioneers and trailblazers of a global IT Market Category (DEX) that is shaping the future of how the world works, giving our customers' IT Teams total digital visibility across their enterprise. Our innovative solutions integrate real-time analytics, automation, and employee feedback across all endpoints. This enables our IT teams to solve complex technical challenges, create ever more productive workplaces, and deliver happy, satisfied employees in the digital workplace. With over 1000 employees across 5 continents, Nexthink operates as One Team, connecting, collaborating and innovating to continuously grow. We call our employees 'Nexthinkers' and our commitment to diversity, inclusion, and equity is second to none. We currently have over 75 nationalities working with us, from all cultures and backgrounds, speaking many different languages. If you are looking for a change and like a nice atmosphere, lots of challenges, and having fun while working, this is a great opportunity for you! Check what we offer: - Permanent Contract and a competitive compensation package. - Beautiful office with a view of Lake Geneva, conveniently located next to the Prilly-Malley train station - Hybrid work model balancing office and remote work, with a structured approach for new hires to foster connections and onboarding. - Flexible Hours and unlimited vacation (employees have unlimited paid time off on top of the 25 days of holidays we offer) plus 3 company-paid volunteer days. - Free access to a fitness centre inside the building. - Reimbursement of the half-fare travel card for public transport. - ‍ Reimbursement up to 50% of the cost of French classes. - Fresh fruit, cookies, and soft drinks as well. - Regular company and team events like Voluntary Days, Pizza talks, Team Building activities, hosting Meetups at the office and more! - Bonuses for referring successful hires after three months of continuous employment. - We offer a relocation package to people who are coming from another country. Please note that not all the benefits listed above are available for temporary, contract, and internship roles. To ensure you have the most up-to-date information, we recommend checking with your Recruitment Partner.