Expedia Group
Senior Machine Learning Engineer – Universal Messaging Platform
Location: San Jose, CA (Travelers, partners, and employees)
Pay range: $187,000 – $261,500 per year, with potential to grow up to $299,000 based on performance.
In This Role, You Will
Collaborate closely with ML scientists to productize and scale machine‑learning models from experimentation to production.
Design, build, and own large‑scale, distributed ML systems for training, deployment, inference, and monitoring.
Lead design discussions and architecture reviews; drive high‑impact engineering decisions for ML platforms and applications.
Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality.
Develop and maintain reusable components, libraries, and tools to accelerate the ML development lifecycle.
Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities.
Ensure scalability, observability, and fault‑tolerance across all ML stack components.
Promote engineering excellence by advocating best practices in testing, CI/CD, infrastructure‑as‑code, and monitoring.
Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals.
Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learning to improve systems and processes.
Minimum Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
8+ years of experience in software or ML engineering, delivering scalable ML solutions.
Strong programming skills in Python, Scala, or Java.
Deep experience building and maintaining production‑grade ML pipelines and infrastructure.
Expertise in MLOps practices, including model lifecycle management, versioning, monitoring, and CI/CD for ML.
Experience with big data ecosystems (Spark, Hive, Databricks, Delta Lake) and streaming technologies.
Proficiency with ML frameworks such as TensorFlow, PyTorch, or XGBoost.
Experience working in cloud‑based environments (AWS, GCP, or Azure) and with infrastructure‑as‑code tools.
Hands‑on experience with orchestration tools like Flyte, Airflow, or Kubeflow.
Proficient in containerization and orchestration technologies such as Docker and Kubernetes.
Preferred Qualifications
Collaborative, curious, and eager to explore new paradigms in AI.
Passion for mentoring, continuous learning, and contributing to a high‑performance engineering culture.
Demonstrated ability to lead cross‑functional projects and influence technical direction across teams.
Experience with automated testing across different layers (unit, integration, functional).
Familiarity with advanced ML techniques, including deep learning, NLP, recommendation systems, and generative AI.
Experience designing or implementing multi‑agent architectures for autonomous collaboration and decision‑making.
Understanding of agent planning, memory, tool use, and self‑reflection mechanisms.
Benefits
Full benefits package including medical, dental, vision, and paid time off.
Wellness & travel reimbursement, travel discounts, and International Airlines Travel Agent (IATAN) membership.
Generous parental leave and flexible work model with optional office options.
Career development resources and a culture that celebrates diversity and belonging.
Accommodation Requests If you need assistance with any part of the application or recruiting process due to a disability, please contact our Recruiting Accommodations Team through the Accommodation Request portal.
Equal Employment Opportunity Statement Expedia is an employer of choice. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status. This employer participates in E‑Verify.
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Pay range: $187,000 – $261,500 per year, with potential to grow up to $299,000 based on performance.
In This Role, You Will
Collaborate closely with ML scientists to productize and scale machine‑learning models from experimentation to production.
Design, build, and own large‑scale, distributed ML systems for training, deployment, inference, and monitoring.
Lead design discussions and architecture reviews; drive high‑impact engineering decisions for ML platforms and applications.
Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality.
Develop and maintain reusable components, libraries, and tools to accelerate the ML development lifecycle.
Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities.
Ensure scalability, observability, and fault‑tolerance across all ML stack components.
Promote engineering excellence by advocating best practices in testing, CI/CD, infrastructure‑as‑code, and monitoring.
Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals.
Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learning to improve systems and processes.
Minimum Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
8+ years of experience in software or ML engineering, delivering scalable ML solutions.
Strong programming skills in Python, Scala, or Java.
Deep experience building and maintaining production‑grade ML pipelines and infrastructure.
Expertise in MLOps practices, including model lifecycle management, versioning, monitoring, and CI/CD for ML.
Experience with big data ecosystems (Spark, Hive, Databricks, Delta Lake) and streaming technologies.
Proficiency with ML frameworks such as TensorFlow, PyTorch, or XGBoost.
Experience working in cloud‑based environments (AWS, GCP, or Azure) and with infrastructure‑as‑code tools.
Hands‑on experience with orchestration tools like Flyte, Airflow, or Kubeflow.
Proficient in containerization and orchestration technologies such as Docker and Kubernetes.
Preferred Qualifications
Collaborative, curious, and eager to explore new paradigms in AI.
Passion for mentoring, continuous learning, and contributing to a high‑performance engineering culture.
Demonstrated ability to lead cross‑functional projects and influence technical direction across teams.
Experience with automated testing across different layers (unit, integration, functional).
Familiarity with advanced ML techniques, including deep learning, NLP, recommendation systems, and generative AI.
Experience designing or implementing multi‑agent architectures for autonomous collaboration and decision‑making.
Understanding of agent planning, memory, tool use, and self‑reflection mechanisms.
Benefits
Full benefits package including medical, dental, vision, and paid time off.
Wellness & travel reimbursement, travel discounts, and International Airlines Travel Agent (IATAN) membership.
Generous parental leave and flexible work model with optional office options.
Career development resources and a culture that celebrates diversity and belonging.
Accommodation Requests If you need assistance with any part of the application or recruiting process due to a disability, please contact our Recruiting Accommodations Team through the Accommodation Request portal.
Equal Employment Opportunity Statement Expedia is an employer of choice. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, sexual orientation, national origin, genetics, disability, age, or veteran status. This employer participates in E‑Verify.
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