Manhattan WMOS Engineer
ShyftLabs
Support Manhattan WMOS implementations and operations as a WMS Engineer at ShyftLabs. ShyftLabs builds data products and digital solutions for Fortune 500 companies.
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The Walt Disney Company — оқиғалар, кейіпкерлер және қонақтарға ұсынылатын әсерлер негізінде құрылған жаһандық ойын-сауық компаниясы. Оның қызметі тақырыптық саябақтар мен демалыс орындарын, медиа желілерді, киностудияларды және интерактивті медианы қамтиды. Компанияның өнімдері мен әсерлері әлем бойынша әртүрлі платформалар мен құрылғылар арқылы аудиторияға жетеді. 1923 жылы құрылған Disney ойын-сауық, қонақжайлық, саяхат, тамақ және сусын салаларындағы ірі халықаралық жұмыс күшін біріктіре отырып, тұтынушылардың деректерін өңдеуде құпиялылыққа, ашықтыққа және талаптарға сәйкестікке баса назар аударады.
Software Engineer II, ML Observability
Build machine learning anomaly detection and observability systems for Disney+, Hulu, and ESPN. Develop production models, telemetry pipelines, inference services, and AI-powered operational insights.
Сипаттама
- Contribute to production anomaly detection models, including autoencoders, statistical threshold models, and ensemble systems that monitor thousands of microservices.
- Develop and improve ML training pipelines with PyTorch on GPU clusters.
- Engineer features, train models, calibrate thresholds, and deploy models through MLflow.
- Build features from time-series telemetry using windowing, normalization, and data quality safeguards.
- Support real-time ML inference, including model serving, detection logic, and alert generation.
- Develop AI capabilities using foundation models such as Claude and GPT-4.
- Create and deploy scalable FastAPI services for ML predictions, health status, and insights.
- Work across teams to integrate ML intelligence into incident response and release validation workflows.
- Help evaluate and retrain models and tune thresholds.
- Deliver features from design through implementation, take part in model design and code reviews, and expand ownership of production ML system components.
Талаптар
- At least 3 years of professional software engineering experience building, scaling, and maintaining ML-powered backends, data-driven microservices, and production REST APIs with FastAPI or Flask.
- Hands-on experience across the ML engineering lifecycle using PyTorch or TensorFlow, including choosing model architectures, engineering features, training, and evaluation.
- Proficiency in Python and at least one machine learning framework.
- Experience managing experiments, model lineage, and hyperparameter tracking with MLflow or Weights & Biases.
- Experience processing, transforming, and querying large telemetry, event, or time-series datasets with PySpark, Pandas, or Databricks.
- Experience with GitHub, Docker, and cloud-native deployments such as AWS/EKS.
- Strong analytical and troubleshooting abilities.
- Strong collaboration and communication skills, including the ability to work across teams.
- Preferred experience integrating foundation models, engineering and evaluating prompts, working with RAG architectures, or using LangChain or LangGraph.
- Preferred experience with observability platforms such as Datadog, Grafana, or Conviva.
- Preferred experience with large-scale data platforms such as Databricks, Spark, or Snowflake.
- Bachelor’s degree in Computer Science, Machine Learning, Statistics, or Engineering, or equivalent experience.
Артықшылықтар
- A bonus and/or long-term incentive units may be offered.
- Medical benefits.
- Financial benefits.
- Other benefits depend on the level and position offered.
- Primarily on-site work with occasional work from home.
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