Mayflower is a technology company building high-load products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, we solve complex engineering challenges and create solutions that power real-time entertainment for a global audience. We are looking for a Lead ML Engineer to own a new ML stream focused on fast delivery of applied machine learning solutions across different product and business domains. The stream will work with a broad range of ML challenges. Some initiatives may be relatively small and delivered within a few weeks, while others may prove their value and grow into larger dedicated projects. This role combines hands-on ML engineering with end-to-end technical delivery ownership. You will receive product problems and expected outcomes from Product or internal stakeholders, clarify the technical requirements and constraints, define the implementation approach, coordinate execution within the stream, and bring solutions to production readiness and launch. This is a highly hands-on role. The stream will not have a dedicated software engineer for every initiative, so we expect Data Scientists and ML Engineers to be comfortable working beyond experimentation and contributing directly to production code. The role is not tied to a single ML domain. We value strong ML fundamentals, engineering skills, pragmatism, and the ability to quickly understand new problem areas more than deep specialization in one particular class of models. Job Responsibilities Technical Delivery Ownership Turn product and business problems into concrete ML implementation plans. Clarify requirements, constraints, available data, integrations, and success criteria together with Product and relevant stakeholders. Define technical scope, milestones, dependencies, risks, and delivery estimates. Select appropriate ML approaches and determine the fastest reliable way to validate and implement them. Drive technical delivery through experimentation, implementation, integration, deployment, and launch readiness. Keep delivery on track, proactively identify blockers, and coordinate dependencies with other teams. Provide Product with clear technical options, trade-offs, estimates, risks, and experiment results required for product decisions. Support production rollout and iteration based on observed results. Hands-on ML & Engineering Design, train, evaluate, and deploy ML models across different domains and problem types. Write production-quality Python and contribute directly to implementation. Build APIs, batch jobs, data-processing pipelines, and ML services required to deliver solutions where appropriate. Work with classical ML, deep learning, and foundation-model-based approaches depending on the problem. Process and transform large production datasets using Python and SQL. Integrate models into existing production systems. Implement appropriate testing, monitoring, logging, and observability for delivered ML solutions. Work within the shared ML infrastructure, architecture, and engineering practices used across the company. Collaborate with Data Science, Backend, Data Engineering, and MLOps specialists when deeper domain expertise or infrastructure changes are required. Stream Execution Break initiatives down into concrete technical tasks and coordinate execution within the stream. Coordinate the work of Data Scientists and ML Engineers contributing to stream initiatives. Review technical approaches, experiments, and implementation. Keep the team focused on the agreed scope, priorities, and delivery timeline. Identify technical risks and dependencies early and drive them to resolution. Escalate architectural, infrastructure, or methodological questions when broader alignment is required. Help prepare successful initiatives for scaling or transition into longer-term ownership if they grow beyond the scope of the stream. Cross-functional Collaboration Work closely with Product throughout the delivery lifecycle. Independently gather the technical details and constraints required to execute on a product request. Communicate estimates, dependencies, technical trade-offs, and delivery status clearly. Work directly with Engineering and other internal teams to unblock implementation. Challenge technically unclear, contradictory, or infeasible requirements and propose practical alternatives.