Principal MLOps Engineer leading design and optimization of machine learning infrastructure at Wood Mackenzie. Collaborating with data science and engineering teams to ensure robust automated ML lifecycles.
TC
Hybrid Lead Machine Learning Engineer, Infrastructure
Posted 5 months ago
About the role
- Lead ML engineer responsible for ML research, deployment, and infrastructure across Disney Entertainment & ESPN. Drive scalable learning, inference, monitoring, and cross-team ML adoption.
Responsibilities
- Lead research, development, deployment, and optimization of ML applications across Disney Entertainment & ESPN
- Collaborate closely with cross-functional teams including Engineering, Product, Data, and Editorial
- Design and develop infrastructure supporting the full cycle of machine learning, including workflow orchestration and management interfaces, data discovery tools, data quality and feature libraries
- Drive infrastructure innovation for scalable learning, inference, and monitoring
- Provide ML consultancy and mentorship and contribute to ML Lab mission enabling ML across heterogeneous environments
- Conduct in-depth data exploration and analysis to support strategic initiatives and shape algorithmic roadmap
- Drive data and ML-driven solutions for use cases such as recommendation systems, object detection, anomaly detection, RAGs and translations
- Identify opportunities to improve business operations and develop solutions to lift business KPIs
- Provide technical leadership to a team of engineers and work collaboratively with peers to achieve goals within deadlines
Requirements
- BS in computer science, statistics, math or a related quantitative field
- 7+ years of relevant SWE and MLEng experience
- Expertise in data science, (deep) learning algorithms, or statistical methods
- Comfortable operating at all levels of the predictive stack, including data collection, feature engineering, batch training and low-latency online serving
- Experience designing and developing backend microservices for large-scale distributed systems using gRPC or REST
- Experience with large-scale distributed data processing systems, cloud infrastructure such as AWS or GCP, and container systems such as Docker or Kubernetes
- Track record of building scalable systems, from design to full production
- Understanding of statistical concepts (e.g., hypothesis testing, regression analysis)
- Excellent written and oral communication skills
- (Preferred) Familiarity with developing and deploying Spark and ML pipelines
- (Preferred) Hands-on experience with big data technologies such as Hadoop, HDFS, Airflow, Databricks, Kinesis, Kafka
Benefits
- A bonus and/or long-term incentive units may be provided as part of the compensation package
- Full range of medical benefits
- Financial benefits
- Other benefits dependent on the level and position offered
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