Software Engineer II – ML, Observability

The Walt Disney Company

. Contribute to production ML models for anomaly detection, including autoencoders, statistical threshold models, and ensemble detection systems monitoring thousands of microservices .

Posted 10/11/2026full-timeUnited StatesMid-LevelSenior💰 $119,200 - $167,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining ML-powered backends and production RESTful APIs, with a strong focus on end-to-end ML engineering using frameworks like PyTorch. Proficient in feature engineering, model evaluation, and deploying scalable services while collaborating cross-functionally to integrate ML intelligence into workflows.

Highest-signal resume keywords

ML EngineeringPyTorchFastAPIModel EvaluationFeature Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningFeature EngineeringModel TrainingThreshold CalibrationData ProcessingPythonRESTful APIsHyperparameter TrackingTime-Series AnalysisModel Deployment

Soft Skills

Analytical SkillsTroubleshooting SkillsCollaborationCommunication

Tools & Technologies

MLflowPySparkPandasDatabricksGitHubDockerAWSEKS

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceMachine LearningStatisticsEngineering

Industry Keywords

Anomaly DetectionAutoencodersEnsemble DetectionFoundation ModelsReal-Time InferenceObservability PlatformsData Platforms

Tech Stack

Tools & technologies

AWSCloudDockerFlaskGrafanaMicroservicesPandasPySparkPythonPyTorchSparkTensorflow

About the role

Key responsibilities & impact

Contribute to production ML models for anomaly detection, including autoencoders, statistical threshold models, and ensemble detection systems monitoring thousands of microservices
Develop and improve ML training pipelines using PyTorch on GPU clusters
Perform feature engineering, model training, threshold calibration, and deployment through MLflow
Engineer features from time-series telemetry, including windowing, normalization, and data quality safeguards
Support real-time ML inference systems, including model serving, detection logic, and alert generation
Build AI-driven capabilities using foundation models such as Claude and GPT-4
Create and deploy scalable FastAPI services delivering ML predictions, health status, and insights
Partner cross-functionally to embed ML intelligence into incident response and release validation workflows
Contribute to model evaluation, retraining, and threshold tuning
Deliver features end-to-end, participate in model design and code reviews, and grow ownership of production ML system components

Requirements

What you’ll need

3+ years of professional software engineering experience building, scaling, and maintaining ML-powered backends, data-driven microservices, and production RESTful APIs using FastAPI or Flask
Strong hands-on experience in end-to-end ML engineering using PyTorch or TensorFlow, including model architecture selection, feature engineering, training, and evaluation
Proficiency in Python and at least one ML framework
Experience managing model experiments, lineage, and hyperparameter tracking using MLflow or Weights & Biases
Experience processing, transforming, and querying large-scale telemetry, event, or time-series datasets using PySpark, Pandas, or Databricks
Experience with GitHub, Docker, and cloud-native deployments such as AWS/EKS
Strong analytical and troubleshooting skills
Strong collaboration and communication skills, with ability to work cross-functionally
Preferred: foundation model integration, prompt engineering/evaluation, RAG architectures, LangChain, or LangGraph
Preferred: observability platforms such as Datadog, Grafana, or Conviva
Preferred: large-scale data platforms such as Databricks, Spark, or Snowflake
Bachelor’s degree in Computer Science, Machine Learning, Statistics, Engineering, or equivalent experience

Benefits

Comp & perks

Bonus and/or long-term incentive units may be provided
Medical benefits
Financial benefits
Other benefits dependent on the level and position offered
Primarily on-site with occasional work from home