Pay
In line with similar jobs
$180K–$220K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Los Angeles, California, United States
Actively hiring, first seen by us 8 days agoHighly competitive compensation plan. Salary range $180,000 - $220,000 annually determined by a myriad of factors including, but not limited to, years of experience, depth of experience, and other relevant business considerations. Employees are eligible for annual discretionary bonus.Read the full posting
This role bridges data science, data engineering, and software engineering. You will work closely with data scientists, data engineers, analysts, product partners, and software developers to translate experimental models into scalable, reliable, governed production systems on Databricks. The position emphasizes practical MLOps, robust feature and training pipelines, reproducibility, observability, and measurable business outcomes. This is a hybrid position based in our Woodland Hills, CA office. You will be required to adhere to our hybrid work policy, working from our office Monday and Thursday plus one additional weekday on a weekly basis. Job Responsibilities:Read the full posting
Build and maintain production ML solutions for forecasting, pricing, inventory, and recommendations
Turn data scientists' notebooks into tested, repeatable training and inference pipelines
Build feature, training, batch-scoring, and real-time inference workflows in Python, Spark, SQL, Databricks, and MLflow
Implement MLOps practices such as CI/CD, model versioning, promotion, and rollback
Monitor model quality, drift, latency, failures, and compute cost
Design, build, deploy, and maintain production-grade machine learning solutions supporting demand forecasting, dynamic pricing, inventory optimization, and recommendation use cases.
Partner with data scientists to convert experimental notebooks and prototypes into modular, tested, repeatable training and inference pipelines.
Build and maintain feature engineering, model-training, batch-scoring, and real-time inference workflows using Python, Spark, SQL, Databricks, and MLflow.
Implement MLOps practices for experiment tracking, model registration and versioning, automated testing, CI/CD, controlled promotion, rollback, and reproducibility.
Define and monitor model and operational health measures, including model quality, drift signals, latency, failures, data quality, resource utilization, and compute cost.
Experience designing batch and real-time model-serving patterns and integrating model outputs into operational applications.
Experience with model explainability techniques, statistical equivalence testing, A/B testing, and business-impact measurement.
Experience with Azure services such as Synapse Analytics, Event Hubs, Azure Data Lake Storage, or related integration services.
Experience with distributed ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or Spark MLlib.
Job Requirements: - Bachelor's degree in Computer Science, Computer Engineering, Data Science, Statistics, Applied Mathematics, or a related field; or equivalent practical experience. - 4+ years of relevant experience in machine learning engineering, applied data science, data engineering, or software engineering for production ML systems.
Viking (NYSE: VIK) is a global leader in experiential travel with a fleet of more than 100 ships, exploring 21 rivers, five oceans and all seven continents.
In the employer’s words · Read in context
Actively hiringFirst seen by us 8 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
In line with similar jobs
$180K–$220K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Day 8
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- Partner with data scientists to convert experimental notebooks and prototypes into modular, tested, repeatable training and inference pipelines. - Build and maintain feature engineering, model-training, batch-scoring, and real-time inference workflows using Python, Spark, SQL, Databricks, and MLflow. - Implement MLOps practices for experiment tracking, model registration and versioning, automated testing, CI/CD, controlled promotion, rollback, and reproducibility.
- 4+ years of relevant experience in machine learning engineering, applied data science, data engineering, or software engineering for production ML systems. - Strong proficiency in Python and SQL, including experience building maintainable, testable production code. - Hands-on experience developing, deploying, and supporting machine learning models in production environments.
- Hands-on experience developing, deploying, and supporting machine learning models in production environments. - Experience with cloud platforms, with a preference for Microsoft Azure. - Experience with Apache Spark or Databricks and large-scale data processing.
- Experience with model explainability techniques, statistical equivalence testing, A/B testing, and business-impact measurement. - Experience with Azure services such as Synapse Analytics, Event Hubs, Azure Data Lake Storage, or related integration services. - Experience with distributed ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or Spark MLlib.
We are seeking a highly motivated and experienced Machine Learning Engineer to join our dynamic and growing Data Platforms & Solutions team. As a Machine Learning Engineer at Viking you will design, productionize, deploy, and operate machine learning solutions that support management decisions. This role bridges data science, data engineering, and software engineering. You will work closely with data scientists, data engineers, analysts, product partners, and software developers to translate experimental models into scalable, reliable, governed production systems on Databricks. The position emphasizes practical MLOps, robust feature and training pipelines, reproducibility, observability, and measurable business outcomes. This is a hybrid position based in our Woodland Hills, CA office. You will be required to adhere to our hybrid work policy, working from our office Monday and Thursday plus one additional weekday on a weekly basis.
- Experience with cloud platforms, with a preference for Microsoft Azure. - Experience with Apache Spark or Databricks and large-scale data processing. - Knowledge of supervised learning, forecasting, optimization, feature engineering, model evaluation, and experiment design.
- Experience with Azure services such as Synapse Analytics, Event Hubs, Azure Data Lake Storage, or related integration services. - Experience with distributed ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, or Spark MLlib. - Experience implementing governed feature stores, lineage, access controls, audit trails, and model approval workflows.
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job Requirements:
Preferred Qualifications:
What We Offer You:
Viking is a certified Great Place to Work company. This certification is a result of our commitment to excellence, integrity and our teams’ outstanding contributions.
About Viking
Viking (NYSE: VIK) is a global leader in experiential travel with a fleet of more than 100 ships, exploring 21 rivers, five oceans and all seven continents. Designed for curious travelers with interests in science, history, culture and cuisine, Chairman and CEO Torstein Hagen often says Viking offers experiences For The Thinking Person™. Viking has more than 450 awards to its name, including being rated #1 for Rivers and #1 for Oceans five years in a row by Condé Nast Traveler in the 2025 Readers’ Choice Awards. Viking is also rated a “World’s Best” by Travel + Leisure—no other travel company has simultaneously received such honors by both publications.
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Highly competitive compensation plan. Salary range $180,000 - $220,000 annually determined by a myriad of factors including, but not limited to, years of experience, depth of experience, and other relevant business considerations. Employees are eligible for annual discretionary bonus.
Los Angeles, California, United States
This role bridges data science, data engineering, and software engineering. You will work closely with data scientists, data engineers, analysts, product partners, and software developers to translate experimental models into scalable, reliable, governed production systems on Databricks. The position emphasizes practical MLOps, robust feature and training pipelines, reproducibility, observability, and measurable business outcomes. This is a hybrid position based in our Woodland Hills, CA office. You will be required to adhere to our hybrid work policy, working from our office Monday and Thursday plus one additional weekday on a weekly basis. Job Responsibilities:
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