layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Sr Machine Learning Engineer at Walt Disney

Owns the design and development of machine learning models, pipelines, and production ML systems.
Drives development of ML components through own and other engineers’ work.
Develops technical solutions that meet specifications and that inform future ML initiatives.
Executes assigned ML development projects and major model improvements using new or existing technologies.
Develops specifications for assigned ML components, projects, or model enhancements.
Reviews and writes code for model training, evaluation, and inference pipelines.
Participates in setting the architectural direction for ML platforms and data infrastructure.
Designs specific ML components for assigned projects, developing specifications for each.
Able to build and lead end-to-end ML workflows from data ingestion through model serving.
Interacts and coordinates deliverables with data science, data engineering, and product teams across the organization.
Designs and develops ML system specifications for assigned projects.
Designs component tasks for assigned projects, developing ML-specific specifications for each.
Serves as a high-level technical resource and “go-to” person for less experienced ML engineers and data scientists, providing technical guidance and oversight.
Leads team members in problem analysis, model debugging, and issue resolution.
Basic Qualifications
5+ years of relevant experience designing, training, and deploying machine learning models in production environments at scale.
Experience with Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
Strong understanding of ML fundamentals including supervised/unsupervised learning, model evaluation, and feature engineering.
Strong expertise in MLOps practices including model versioning, experiment tracking, CI/CD for ML, and model monitoring.
Experience with cloud-based ML services and infrastructure (e.g., AWS SageMaker, EC2, S3).
Experience with data pipeline and orchestration tools (e.g., Airflow, Spark, Kafka).
Familiarity with database and data storage technologies (e.g., DynamoDB, Redshift, NoSQL), containerization (Docker, Kubernetes), and data manipulation tools.
Experience with Snowflake is required.

Sr Machine Learning Engineer

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