Job description

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.

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

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.
Employees are eligible for annual discretionary bonus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonsqlci/cdaimlazure

Platform: phenom
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)