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Machine Learning Engineer

Los Angeles, California, United States

Actively hiring, first seen by us 7 days ago
Pay
$180,000–220,000/year — pay source
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.
Read the full posting
Work setting
Hybrid — work setting source
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
Employment
Not stated

What you’ll work on

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

From the employer’s posting
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.

What you’ll bring

All qualifications

Preferred experience

  • 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.
Qualification wording
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.
Education & alternatives
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.

About Viking Cruises

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

Apply at Viking Cruises

Posting record

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Actively hiringFirst seen by us 7 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 27, 20267 days ago
Recorded sightings
12
Last seen by us
Nov 4, 2026
Employer says posted
Sep 10, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

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.

Time since we first saw it

Day 7

Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Viking Cruises is hiring

3 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • Azure
  • Databricks
  • MLflow
  • Spark
  • LightGBM
  • PyTorch
  • scikit-learn
  • TensorFlow
  • XGBoost
Where the posting names them
- 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.

Your next step

Complete your application on vikingcruises.hire.trakstar.com. The employer’s form will show what is required.

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About the role

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.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

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 description

View original posting ↗

Job Summary
 
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.
 
Job Responsibilities:  
  • 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.
  • Validate model changes against reproducible datasets and established baselines, and assess downstream impact.
  • Integrate model outputs with business applications and downstream platforms through reliable batch jobs, APIs, and secure data-access patterns.
  • Optimize model architecture, feature processing, training, and inference for scalability, reliability, maintainability, and cost efficiency.
  • Implement safeguards and fallback behavior so invalid data or failed optimization does not result in unsafe or misleading recommendations.
  • Collaborate with stakeholders to translate revenue-management requirements into technical specifications, acceptance criteria, and measurable outcomes.
  • Create and maintain technical documentation, model cards, runbooks, deployment plans, monitoring procedures, and retraining and rollback strategies.
  • Ensure solutions comply with enterprise data governance, privacy, security, audit, and responsible AI requirements.

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.
  • 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.
  • 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 software engineering practices including Git, code review, automated testing, CI/CD, configuration management, and environment promotion.
  • Strong problem-solving, critical-thinking, communication, and cross-functional collaboration skills.
  • Ability to work independently, manage changing priorities, and deliver in a fast-paced product environment.

Preferred Qualifications:

  • 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.
  • Experience implementing governed feature stores, lineage, access controls, audit trails, and model approval workflows.

What We Offer You: 

  • 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.
  • 401(k) plan with company match.
  • Employee Share Purchase Plan (ESPP) Viking full-time regular employees working in the United States can purchase Viking shares through payroll deductions.
  • Full benefits including medical, dental, vision, life and disability insurance at a highly subsidized rate (some plans are fully paid by Viking).
  • Accrue 15 paid vacation days, sick time accrual by state, and 6 paid holidays per year.
  • Opportunity to take a free and/or discounted cruise.
  • Highly subsidized gym membership.
  • Discounts on theatres, theme parks, movie tickets, travel discounts through IATA membership and too many more discounts to name.

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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Pay
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.
Location & working pattern

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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