Lead ML Engineer

Stack

Responsibilities

• ▹Turn product and business problems into concrete ML implementation plans
• ▹Clarify requirements, constraints, available data, integrations and success criteria with Product and 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 needed for product decisions
• ▹Support production rollout and iteration based on observed results
• ▹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 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 expertise or infrastructure changes are needed
• ▹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
• ▹Work closely with Product throughout the delivery lifecycle
• ▹Independently gather the technical details and constraints needed 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

About the company

Mayflower is a technology company building high-load products used by millions of people worldwide. It operates at the scale of one of the world's top-50 websites and powers real-time entertainment for a global audience.