Senior Machine Learning Software Engineer Jobs in Ontario, CA

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data Scientists, Data Engineers, and Analytics stakeholders to deploy, maintain, and improve production ML workflows across AWS. The ideal candidate combines strong software engineering and MLOps fundamentals with a passion for building reliable, scalable machine learning systems.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. 
• 3+ years of experience in Machine Learning Engineering, MLOps, Software Engineering, or related technical roles. 
• Strong Python development skills, including experience with pandas, PyTorch, scikit-learn, boto3, and SQL. 
• Experience working with AWS services such as SageMaker, Step Functions, Lambda, S3, IAM, and ECR. 
• Experience developing or supporting orchestration workflows using Airflow, Glue, or similar technologies. 
• Familiarity with cloud-based data platforms such as Snowflake, Redshift, or Athena. 
• Experience with Docker, CI/CD pipelines, source control workflows, and software development best practices. 
• Strong troubleshooting and debugging skills across distributed systems and machine learning workflows. 
• Ability to collaborate effectively with technical and non-technical stakeholders. 

Preferred Qualifications

• Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. 
• Familiarity with MLflow, Hydra/OmegaConf, FastAPI, or similar ML platform tooling. 
• Experience supporting deep learning workflows in production environments. 
• Exposure to infrastructure-as-code tools such as Terraform, Terragrunt, or CloudFormation. 
• Experience working with customer analytics, marketing measurement, or recommendation systems. 

Salary Range: 129,800 - 162,200 annually + bonus eligibility. This is the expected salary range for this position. Ultimately, in determining pay, we'll consider the successful candidate's location, experience, and other job-related factors.

Benefits: Employees (and their eligible family members) may enroll in the following types of insurance coverage: medical, dental, vision, legal, and accidental death and dismemberment, as well as FSA/HSA (depending on enrolled medical plan). Yum! also provides short-term disability, long-term disability, and life insurance. Employees may enroll in our 401(k) plan. Yum! provides 4 weeks of vacation, paid sick leave, 10 paid holidays, a floating day off and 2 paid days for volunteer time each calendar year. To learn more about working at Yum! -Click here. 

At Yum!, one of our core values is to Believe in ALL People. This means seeing the value in everyone and unlocking their full potential to be their best self. YUM! Brands, Inc. (including its subsidiaries Yum Restaurant Services Group, LLC ("YRSG") and Yum Connect, LLC ("Yum Digital and Technology")(collectively, "Yum") is proud to be an equal opportunity employer and is committed to equity, inclusion, and belonging for all dimensions of diversity.  We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other protected characteristic. Yum! is committed to working with and providing reasonable accommodation to applicants with disabilities or special needs.

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Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. 
• 3+ years of experience in Machine Learning Engineering, MLOps, Software Engineering, or related technical roles. 
• Strong Python development skills, including experience with pandas, PyTorch, scikit-learn, boto3, and SQL. 
• Experience working with AWS services such as SageMaker, Step Functions, Lambda, S3, IAM, and ECR. 
• Experience developing or supporting orchestration workflows using Airflow, Glue, or similar technologies. 
• Familiarity with cloud-based data platforms such as Snowflake, Redshift, or Athena. 
• Experience with Docker, CI/CD pipelines, source control workflows, and software development best practices. 
• Strong troubleshooting and debugging skills across distributed systems and machine learning workflows. 
• Ability to collaborate effectively with technical and non-technical stakeholders. 

Preferred Qualifications

• Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. 
• Familiarity with MLflow, Hydra/OmegaConf, FastAPI, or similar ML platform tooling. 
• Experience supporting deep learning workflows in production environments. 
• Exposure to infrastructure-as-code tools such as Terraform, Terragrunt, or CloudFormation. 
• Experience working with customer analytics, marketing measurement, or recommendation systems. 

• Support the deployment, monitoring, and ongoing maintenance of media measurement and customer modeling systems in partnership with Data Science and Engineering teams. 
• Develop and maintain SageMaker processing and training jobs, model endpoints, and supporting infrastructure across development, testing, and production environments. 
• Contribute to Step Functions, Lambda functions, and Airflow (MWAA) workflows that orchestrate model training, scoring, retraining, and analytics pipelines. 
• Support MLflow model registration and promotion processes, configuration management, and versioned model artifacts. 
• Build and maintain Docker images, ECR repositories, and GitLab CI/CD pipelines to enable reliable model deployment and release processes. 
• Help productionize machine learning models and data pipelines that support customer analytics, scoring, and decisioning use cases. 
• Investigate and resolve production issues using CloudWatch, DataDog, SageMaker logs, and workflow monitoring tools. 
• Collaborate with cross-functional partners to implement platform enhancements, improve operational reliability, and deliver new capabilities. 
• Contribute to engineering best practices, documentation, testing strategies, and operational procedures.