Senior Machine Learning Engineer

The Walt Disney Company

. Own the design and development of machine learning models, pipelines, and production ML systems .

Posted 10/9/2026full-timeUnited StatesSenior💰 $137,200 - $202,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, developing, and deploying machine learning models and systems, with a strong focus on MLOps practices and cloud-based infrastructure. Capable of leading teams in technical problem-solving and coordinating cross-functional deliverables.

Highest-signal resume keywords

Machine Learning Model DevelopmentMLOps PracticesPython ProgrammingCloud-Based ML ServicesData Pipeline Orchestration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel EvaluationFeature EngineeringModel VersioningExperiment TrackingCI/CD for MLReal-Time ML InferenceLarge-Scale Recommendation SystemsNatural Language ProcessingComputer Vision

Soft Skills

Problem AnalysisTeam LeadershipIssue Resolution

Tools & Technologies

TensorFlowPyTorchScikit-learnAWS SageMakerAirflowSparkKafkaDockerKubernetesSnowflake

Industry Keywords

Data ScienceData EngineeringModel DebuggingGenerative AINoSQL

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSCloudDockerDynamoDBEC2JavaKafkaKubernetesNoSQLPythonPyTorchScikit-LearnSparkSpringSpring BootSpringBootTensorflow

About the role

Key responsibilities & impact

Own the design and development of machine learning models, pipelines, and production ML systems
Drive development of ML components through own and other engineers’ work
Develop technical solutions that meet specifications and inform future ML initiatives
Execute assigned ML development projects and major model improvements using new or existing technologies
Develop specifications for assigned ML components, projects, or model enhancements
Review and write code for model training, evaluation, and inference pipelines
Participate in setting the architectural direction for ML platforms and data infrastructure
Design specific ML components for assigned projects and develop specifications for each
Build and lead end-to-end ML workflows from data ingestion through model serving
Coordinate deliverables with data science, data engineering, and product teams across the organization
Design and develop ML system specifications for assigned projects
Serve as a high-level technical resource and go-to person for less experienced ML engineers and data scientists
Lead team members in problem analysis, model debugging, and issue resolution

Requirements

What you’ll need

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, such as AWS SageMaker, EC2, and S3
Experience with data pipeline and orchestration tools, such as Airflow, Spark, and Kafka
Familiarity with database and data storage technologies, such as DynamoDB, Redshift, and NoSQL
Familiarity with containerization, including Docker and Kubernetes
Familiarity with data manipulation tools
Experience with Snowflake is required
Experience with large-scale recommendation systems, personalization, NLP, or computer vision
Experience with real-time ML inference and low-latency serving architectures
Familiarity with LLMs and generative AI integration in production systems
Experience with Java, such as Spring Boot, is a plus
Bachelor’s degree in Computer Science, Statistics, Mathematics, or similar field, or related work experience

Benefits

Comp & perks

Bonus and/or long-term incentive units may be provided as part of the compensation package
Medical benefits
Financial benefits
Other benefits