Scientific AI & ML Engineer designing and deploying innovative AI - driven solutions. Collaborating with teams to solve complex scientific challenges through advanced machine learning techniques.
TC
Hybrid Lead Machine Learning Engineer, Infrastructure
Posted 3 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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