Machine Learning Engineer
Machine Learning Engineer on Viking's Data Platforms & Solutions team in Los Angeles, designing, deploying, and operating production ML systems for demand forecasting, pricing, inventory, and recommendations. Core stack is Python, SQL, Spark, Databricks, and MLflow with a strong MLOps focus.
- Compensation
- $155K–$180K / yr
- Location
- Los Angeles, California, United States
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 4+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, Computer Engineering, Data Science, Statistics, Applied Mathematics, or related field, or equivalent practical experience
- 4+ years of relevant experience in ML engineering, applied data science, data engineering, or software engineering for production ML systems
- Strong proficiency in Python and SQL, including building maintainable, testable production code
- Hands-on experience developing, deploying, and supporting ML 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 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 environment
Nice to have
- Experience designing batch and real-time model-serving patterns and integrating model outputs into operational applications
- Experience with model explainability, statistical equivalence testing, A/B testing, and business-impact measurement
- Experience with Azure services such as Synapse Analytics, Event Hubs, or Azure Data Lake Storage
- Experience with distributed ML libraries 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
Full description
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Location: Los Angeles,California,United States
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.