Data Science & ML Ops Engineer

CarParts.com-posted 3 months ago
$120,000 - $145,000/Yr
Mid Level
Torrance, CA
1,001-5,000 employees
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CarParts.com 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 productionizing 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, GitHub Actions, AWS CodePipeline) 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, LightGBM, or CatBoost, 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, GitHub Actions, AWS CodePipeline).
  • 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 experience in e-commerce, logistics, or digital marketplaces.
  • Exposure to cloud platforms (AWS, GCP, Azure) for ML deployments.
  • Competitive salary range of $120,000-$145,000.
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