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Senior Machine Learning Engineer
The Walt Disney Company. Conceive, design, develop, train, and deploy machine learning models, pipelines, and systems of moderate to high complexity .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, developing, and deploying machine learning models and systems, with a strong focus on MLOps practices and cloud-based ML infrastructure. Capable of leading teams in problem-solving and model optimization while collaborating effectively across data science and engineering disciplines.
Highest-signal resume keywords
Machine Learning Model DevelopmentMLOps PracticesPython ProgrammingCloud-Based ML ServicesData Pipeline Tools
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningModel EvaluationFeature EngineeringSupervised LearningUnsupervised LearningModel VersioningExperiment TrackingReal-Time ML InferenceLarge-Scale Recommendation SystemsNatural Language Processing
Soft Skills
Problem AnalysisTeam LeadershipTechnical Resource
Tools & Technologies
TensorFlowPyTorchScikit-learnAWS SageMakerAirflowSparkKafkaDockerKubernetesSnowflake
Industry Keywords
Data InfrastructureModel DebuggingLow-Latency Serving ArchitecturesGenerative AI IntegrationComputer Vision
Tech Stack
Tools & technologiesAirflowAmazon RedshiftAWSCloudDockerDynamoDBEC2JavaKafkaKubernetesNoSQLPythonPyTorchScikit-LearnSparkSpringSpring BootSpringBootTensorflow
About the role
Key responsibilities & impact- Conceive, design, develop, train, and deploy machine learning models, pipelines, and systems of moderate to high complexity
- 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
- Build and lead end-to-end ML workflows from data ingestion through model serving
- Interact and coordinate deliverables with data science, data engineering, and product teams across the organization
- 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 such as 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- A bonus and/or long-term incentive units may be provided as part of the compensation package
- Medical, financial, and/or other benefits