ml engineer for real-time entertainment

HireHi; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Mayflower ищет инженера ML для превращения бизнес-проблем в конкретные планы и реализации. Вы будете работать над настройкой и внедрением ML-решений в продакшн, проектировать пайплайны, API и сервисы на Python, а также обеспечивать мониторинг и качество выполнения.

Требуется опыт вывода решений от требований до эксплуатации, сильные навыки Python, Docker и работы с большими данными. Возможна временная удалённая работа с перспективой релокации в Лимассол.

Qualifications

• 5+ лет коммерческого опыта в машинном обучении, data science или ML-инжиниринге.
• Сильные практические навыки Python и разработки ПО.
• Опыт вывода ML-решений от требований до продa производственной среды.
• Глубокое понимание методов ML, статистики, экспериментов и оценки моделей.
• Опыт написания поддерживаемого продакшн-кода, работа с API/пакетами данных.
• Опыт работы с Docker и окружениями продакшн-деплоймента.
• Умение превращать частично определённые задачи в конкретные технические планы.
• Умение оценивать работу, зависимости и риски, вести исполнение в срок.

Responsibilities

• Преобразование продуктовых задач в конкретные планы ML-реализации.
• Установление требований, данных, интеграций и критериев успеха.
• Определение объёма работ, зависимостей, рисков и сроков.
• Выбор подходов к ML и верификация решений на практике.
• Руководство техническим выполнением через эксперименты, внедрение и запуск.
• Согласование с продуктом технических вариантов, trade-off и результатов экспериментов.

Skills

Python

ML engineering

SQL

Docker

API приоритет

Experimentation

Production Code

Docker

Kubernetes

CI/CD

Tools

Kubernetes

MLflow

MLflow

Kafka

CI/CD

Job description

Описание

Mayflower is a technology company building high-load products used by millions of people worldwide. Its products power real-time entertainment for a global audience.

Задачи

• Turn product and business problems into concrete ML implementation plans
• Clarify requirements, constraints, available data, integrations, and success criteria with Product and relevant stakeholders
• Define technical scope, milestones, dependencies, risks, and delivery estimates
• Select appropriate ML approaches and determine reliable ways to validate and implement them quickly
• 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 technical options, trade-offs, estimates, risks, and experiment results for product decisions
• Support production rollout and iteration based on observed results
• Design, train, evaluate, and deploy ML models across 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 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 required
• Break initiatives 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 agreed scope, priorities, and delivery timelines
• 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 to longer-term ownership
• Work closely with Product throughout the delivery lifecycle
• Independently gather the technical details and constraints required to execute product requests
• Communicate estimates, dependencies, technical trade-offs, and delivery status clearly
• Work directly with Engineering and other internal teams to unblock implementation
• Challenge unclear, contradictory, or infeasible requirements and propose practical alternatives

Требования

• 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 product requirements 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 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
• Будет плюсом: previous experience as a Tech Lead, Stream Lead, or technical owner of ML initiatives, Kubernetes and CI/CD, Kafka or other streaming platforms, Airflow, MLflow, experiment tracking, model monitoring or similar tooling, real-time or high-load ML services, 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, 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

Условия

Temporary remote work with future relocation to Limassol