Lead ML Engineer
Confirmed on the employer's own hiring board on Sep 30, 2026. First seen by Alion on Sep 29, 2026. Mayflower scores B on the Alion truth index.
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.
Requirements
You’ll thrive here if you have
5+ years of commercial experience in Machine Learning, Data Science, or ML Engineering.
Strong hands-on Python programming and software engineering skills.
Experience taking ML solutions from a product requirement through experimentation, implementation, integration, and production.
Strong understanding of machine learning methods, statistics, experimentation, and model evaluation.
Experience writing maintainable production code rather than working exclusively in notebooks.
Experience building APIs, services, batch processing, or data pipelines.
Strong SQL skills and experience working with large production datasets.
Practical experience with Docker and production deployment environments.
Ability to turn partially defined problems into concrete technical plans.
Ability to estimate work, identify dependencies and risks, and drive technical execution against a timeline.
Experience owning technical delivery involving several contributors and coordinating work across dependencies.
Experience reviewing code and technical approaches.
Ability to work effectively within established engineering and ML practices while independently owning delivery within a stream.
Strong communication skills and the ability to work directly with Product and technical stakeholders.
Ability to balance speed and engineering quality: validate ideas quickly when uncertainty is high and build robust solutions when moving towards production.
Nice to Have
Previous experience as a Tech Lead, Stream Lead, or technical owner of ML initiatives.
Experience with Kubernetes and CI/CD.
Experience with Kafka or other streaming platforms.
Experience with Airflow, MLflow, experiment tracking, model monitoring, or similar tooling.
Experience building real-time or high-load ML services.
Experience with FastAPI or similar Python service frameworks.
Experience across several ML domains, such as recommendation systems, ranking, NLP/LLMs, Computer Vision, anomaly detection, forecasting, or classification.
Experience with LLM inference, fine-tuning or other GenAI systems.
Experience in teams where Data Scientists and ML Engineers own a substantial part of production implementation themselves.