This role pays less than
92%
of similar roles. Most pay
$197,925–$254,750
— the shaded band above.
At the midpoint, this role pays about
$158k
versus about
$226k
for comparable roles.
Based on 240 similar postings.
Employer
About The Walt Disney Company
The Walt Disney Company is a diversified global entertainment and media enterprise operating in segments including Disney Parks, Experiences and Products; Entertainment (ABC, Hulu, Disney+); and ESPN. Industry: Entertainment & Media
The Walt Disney Company currently has
118 open roles
on FindRole.
Listed pay typically runs
$148,700–$199,400
across 118 roles with salary data.
Sr Machine Learning Engineer joins the DXT AI Technology Platform team to build an AI enablement platform providing streamlined Generative AI capabilities for the segment. This role involves designing, developing, and deploying enterprise-grade solutions including agentic systems, multi-modal models, RAG, and Responsible AI applications. The engineer will manage the operational backbone of the platform by building CI/CD pipelines, model versioning, and observability infrastructure to monitor performance, drift, and data quality. Key responsibilities include implementing LLMOps best practices, establishing evaluation frameworks for Large Language Models, and developing multi-step agentic workflows. Required skills include expert Python proficiency, experience with cloud platforms like AWS, Azure, or GCP, and expertise in prompt engineering and traditional machine learning. Preferred technical competencies include container orchestration using Docker and Kubernetes, infrastructure-as-code via Terraform, and working with vector databases.
What does a Machine Learning Engineer earn?
Median $236000 from 319 postings across 54 companies.
Design and manage CI/CD pipelines for model versioning, automated deployment, and release management.
Build and maintain observability infrastructure to monitor model performance, data quality, latency, and cost.
Develop production-scale AI systems including multi-step agentic workflows and multi-agent orchestration platforms.
Implement Responsible AI frameworks featuring hallucination detection, safety guardrails, and evaluation systems.
Establish evaluation frameworks for Large Language Models to measure quality, task success, and safety compliance.
Champion LLMOps and MLOps best practices including infrastructure-as-code and automated testing across the platform.
Lead architectural decisions and technical problem-solving for high-complexity AI development and production deployment.
Research and integrate emerging AI technologies and frameworks to drive platform innovation.
What we're looking for
Bachelor's degree in Computer Science, Machine Learning, Mathematical Sciences, Information Systems, Software, Electrical or Electronics Engineering, or a comparable field.
Master's degree or Ph.D. in Artificial Intelligence, Machine Learning, Mathematical Sciences, Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or a comparable field (preferred).
5+ years of experience designing, building, and deploying AI/ML solutions at scale.
1–2 years of production experience with Generative AI technologies.
Expert-level programming proficiency in Python and the AI/ML development ecosystem.
Comprehensive MLOps/LLMOps experience including CI/CD pipelines, model versioning, observability, and lifecycle management.
Production deployment experience on major cloud platforms (AWS, Azure, or GCP).
Experience with container orchestration, infrastructure-as-code, vector databases, and embedding technologies (preferred).
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Computer Vision
Deep Learning
Content Moderation
Adversarial Training
Search Ranking
Information Retrieval
Multimodal Content Filtering
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Computer Vision
Deep Learning
Search Ranking
Retrieval Pipelines
Adversarial Training
Content Moderation
Red-teaming
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Computer Vision
Deep Learning
Content Moderation
Adversarial Training
Search Ranking
Information Retrieval
Machine Learning
Natural Language Processing
PyTorch
TensorFlow
JAX
Python
Go
Java
C++
Computer Vision
Deep Learning
Content Moderation
Adversarial Training
Search Ranking
Information Retrieval
Multimodal Content Filtering