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Director of Machine Learning Engineering
Не указаноПолная занятостьAI & Machine LearningEnterprise Software
Lead production engineering for an AI market model, overseeing MLOps, machine learning engineering, and data engineering. Own the lifecycle from data pipelines and model training through serving and production monitoring.
Обязанности
- Set priorities across production engineering functions and assess technical trade-offs.
- Manage functional leads and hold teams accountable for commitments.
- Own production delivery, reliability, incident response, and infrastructure costs.
- Oversee infrastructure for model training, serving, and large-scale data processing.
- Define group KPIs, track performance, and report progress and risks to senior management, product, and commercial teams.
- Partner with applied research to bring research results into production.
- Hire and retain senior engineers and determine team staffing.
- Set engineering standards for CI/CD, automated testing, observability, and model monitoring.
Требования
- At least 8 years of engineering experience, including 5 or more years leading teams and managers.
- Experience leading projects across multiple groups with interdependent work.
- Production experience with time-series forecasting, reinforcement learning, or large-scale optimization.
- Experience setting direction for a machine learning or data engineering group.
- Strong knowledge of production ML systems to review architecture decisions with senior engineers.
- Experience holding managers accountable for delivery dates and team metrics.
- Hands-on familiarity with Python, PyTorch, Google Cloud Platform, and orchestration tools such as Dask and Dagster.
- Bachelor's degree in computer science, engineering, mathematics, or a related field.
- Ability to communicate technical decisions to commercial teams and commercial constraints to engineers.
Будет плюсом
- MSc or PhD in computer science, machine learning, statistics, engineering, or a related field.
- Experience taking a machine learning platform from prototype to business-critical production.
- Experience in airlines, travel, revenue management, or dynamic pricing.
- Familiarity with real-time inference and high-volume data processing.