Data Science & ML Ops Engineer

Job Description

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What We Do

is the go-to eCommerce platform for auto care and maintenance. We offer drivers quality parts at competitive prices and allow customers to schedule an appointment with a trusted mechanic directly from our website. We use world-class design principles and the latest technologies to deliver a fast, easy-to-use, and mobile-intuitive website. And with our company-owned national distribution network, we bring the best brands and manufacturers directly to consumers, cutting out the costs associated with brick-and-mortar retailers.

Our more than 1,700 team members worldwide are dedicated to Empowering Drivers Along Their Journey.

Our Culture

At , we’re DRIVEN by our core values:
Safety First, Customer Focused, and a Commitment to Excellence. These values guide us in everything we do and push us further in our growth both as a company and as people. They cultivate an environment where employees are inspired and empowered as we build and thrive together. All the hard work of our entire team has resulted in back-to-back record-breaking quarters since Q1 of 2020, as well as our inclusion in the Los Angeles Business Journal’s top 100 Best Places to Work in Los Angeles list for four years in a row (2020, 2021, 2022, 2023).

Join us!

is seeking a Data Science & ML Ops Engineer with strong foundations in software engineering and applied machine learning operations. The ideal candidate will bridge the gap between data science and engineering by designing, deploying, and maintaining robust ML infrastructure, automation systems, and APIs that support intelligent, data-driven decision-making. This role requires someone with deep technical expertise in product ionizing models, developing scalable services, and ensuring reliability of ML systems in real-world environments.

• Build, deploy, and maintain scalable ML pipelines and inference systems for production environments.
• Develop APIs and automation services using AWS Lambda, FastAPI, or Flask to deliver ML solutions at scale.
• Implement model versioning, serialization (Pickle, ONNX, joblib), and deployment best practices.
• Collaborate with data scientists to operationalize ML models, ensuring reproducibility and reliability in production.
• Design and support data-driven automation systems and decision-support tools for business operations.
• Integrate message queueing systems (AWS SQS or similar) for robust communication across services.
• Write unit tests, integration tests, and ensure maintainable code using PyTest or similar frameworks.
• Containerize services with Docker and manage deployments across cloud environments.
• Contribute to CI/CD pipelines (Jenkins, Git Hub Actions, AWS Code Pipeline) for ML systems.
• Implement logging, error handling, monitoring, and alerting for ML services and APIs.

• Bachelor’s degree in Computer Science, Engineering, or related quantitative field with 2–3 years of professional experience.
• Strong proficiency in Python for software engineering, with some knowledge of JavaScript.
• Hands-on experience with ML libraries such as scikit-learn, XGBoost, Light
  
  GBM, or Cat Boost, including inference pipelines.
• Proven experience building APIs and services using FastAPI, Flask, or AWS Lambda.
• Knowledge of RESTful services, JSON, and HTTP methods.
• Experience with model versioning and serialization frameworks (Pickle, ONNX, joblib).
• Familiar with containerization tools such as Docker for deploying ML services.
• Version control expertise with Git, including collaborative workflows (pull requests, branching, code reviews).
• Basic understanding of CI/CD practices and pipelines (Jenkins, Git Hub Actions, AWS Code Pipeline).
• Strong problem-solving skills with ability to implement robust logging, monitoring, and error handling in production.

• Master’s degree in Computer Science, Data Science, or related technical field.
• Experience with MLOps frameworks and lifecycle management tools.
• Familiarity with orchestration tools like Kubernetes, Airflow, or Databricks.
• Prior…