• Delivery and technical leadership • Lead the architecture and hands-on implementation of end-to-end ML systems: data ingestion, pipelines, feature stores, training, evaluation, serving, and monitoring • Own technical decisions across the full stack, data platform, training environment, model serving, and MLOps tooling • Set engineering standards for ML projects: experiment tracking, model versioning, reproducibility, governance, observability, drift monitoring, and CI/CD for ML • Coach and uplift other engineers on the team in modern ML and MLOps practices • Stay accountable for quality, security, and operational soundness of what we ship • Pre-Sales and pipeline support • Partner with the sales leadership team across pre-sales activity: discovery calls, scoping workshops, technical briefings, and LOE preparation • Lead architecture and solutioning conversations with prospects and customers, translate business problems into credible, defensible technical approaches • Provide dedicated technical support to opportunities flowing through the partners sales process, including positioning their products as part of broader data and AI architectures, joint solutioning sessions, and partner-aligned proposals • Contribute to thought leadership and demand generation: blog posts, webinars, capability decks, conference talks, and reference architectures

Role Description Wolt’s Personalization team is responsible for creating a tailored experience for Wolt’s customers across their shopping journey, selecting the best restaurants, dishes or items to match their culinary and shopping preferences across multiple premises such as Discovery, In Venue or Checkout. The Personalization team owns the ML stack, models and integrations that generate real-time recommendations for millions of customers across all Wolt markets. As a Machine Learning (ML) Engineer in Wolt’s Personalization team you will: - Build the ML infrastructure to develop, train and deploy Wolt’s ranking models that select the content to display to our customers; - Work end-to-end, from use case design to implementation, delivery and monitoring of your solutions; - Maintain our production ML stack and raise the team’s ML engineering excellence bar; - Liaise with Wolt’s ML Platform team to adopt different ML technologies and to create technical requirements for their solutions; - Contribute to Wolt ML Engineering and Applied Science communities; - Be part of a cross-disciplinary team with Applied Scientists, Software Engineers and Analysts to provide solutions to customer problems with a direct impact on the company’s business KPIs; - Work at Wolt’s scale: Wolt operates in 30 different markets with millions of customers. 📍This role can be based in one of our tech hubs in Berlin, Helsinki, or Stockholm, or you can work remotely anywhere in Finland, Sweden, Germany. Qualifications - You are experienced in end-to-end machine learning deployments and maintenance of ML systems and have at least 2+ years of experience in ML/MLOps; - You have deployed and ran ML models in production at scale, maybe with hundreds of RPS and low latency; - You bring solid experience in scaling solutions, monitoring ML stacks and troubleshooting ML deployments to the table; - You are experienced in implementing real-time inference ML models in production; - Good understanding of ML and MLOps principles as well as Software engineering experience in Python should complete your profile; - Experienced in Docker, Kubernetes, workflow orchestration tools (e.g. Flyte), model and experiment registries (e.g. MLflow) and model serving systems (e.g. Seldon); - You have solid communication and collaboration skills and are experienced in coordinating initiatives with your team and main stakeholders. Our Commitment to Diversity and Inclusion We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

• Lead the development and deployment of advanced ML models and scalable systems that power critical business decisions • Develop production-ready machine learning systems with robust testing and scalable architecture • Build and improve forecasting and optimization models using XGBoost and advanced ML techniques • Design data pipelines and ML workflows that transform raw data into business intelligence • Bring research into production through experimentation, iteration, and deployment • Integrate ML systems into real business operations and user-facing tools • Drive model explainability initiatives using SHAP values and LLM-powered insights • Collaborate closely with Growth, Marketing Science, Product, and Engineering teams • Influence technical strategy while aligning ML initiatives with business impact • Mentor team members and help elevate engineering and ML standards across the organization

Role Description Workiva is seeking a Senior Applied Machine Learning Scientist to join our innovative team. In this role, you will deliver high-quality technology solutions, guide teams through complex challenges, and play a pivotal role in defining the medium-to-long-term technical strategy for impactful ML initiatives. If you excel in solving ambiguous problems, thrive in collaborative environments, and are passionate about applying machine learning to deliver business value, this opportunity is for you. Join us to tackle cutting-edge challenges and make an impact through innovative machine learning and AI applications. What You’ll Do - Own and deliver high-quality AI & ML solutions that drive business impact and align with team objectives - Develop and maintain scalable infrastructure and libraries specific to Workiva science - Collaborate with stakeholders to define project goals and solve ambiguous, open-ended problems - Lead technical initiatives, defining blueprints for complex projects involving cross-functional teams - Provide guidance on tradeoffs between thorough technical solutions and applied business value, reducing the time between POC and ROI - Mentor and guide team members, fostering a collaborative and high-performing team environment - Establish and promote best practices in MLOps, cloud infrastructure, and operational processes as it relates to the model development lifecycle - Write, review, and maintain efficient, testable, and adaptable code - Proactively optimize existing systems, reducing technical debt and ensuring scalability - Contribute to strategic planning for ML-driven initiatives and influence the team’s technical direction - Champion and evolve the culture of experimentation and measurement, ensuring sound decision making for AI and ML features in product Qualifications - Bachelor’s degree or Advanced degree (Master’s or PhD) in Computer Science, Data Science, or a related field, or equivalent experience - 2+ years of professional experience in applied machine learning or AI science Requirements - Experience designing, implementing, and deploying ML solutions - Expertise in Python, R, or similar programming languages - Experience with MLOps practices and cloud platforms (AWS, Azure, GCP) - Proficiency in experimentation and measurement - Experience leading ML modeling and solution projects, including tuning/training LLM/SLMs - Effective problem-solving skills and ability to address complex technical challenges - Excellent communication and collaboration abilities across teams - Familiarity with TensorFlow, PyTorch, or Scikit-learn - Familiarity with statistical libraries (SciPy, Statsmodels, etc.) - Knowledge of APIs and integration frameworks for scalable solutions - Proven ability to mentor junior team members and establish best practices - Experience optimizing systems for efficiency and scalability - Strong understanding of customer needs and translating them into actionable ML solutions - Excellent problem-solving skills and ability to navigate ambiguous technical challenges - Effective communication and collaboration skills with a track record of working across teams Benefits - Salary range in the US: $141,000.00 - $226,000.00 - A discretionary bonus typically paid annually - Restricted Stock Units granted at time of hire - 401(k) match and comprehensive employee benefits package