Machine Learning Platform Software Engineer

Job description

  • Collaborate with data engineers, data scientists, and product teams to guide the translation of R&D prototypes into stable, testable, and maintainable production services
  • Develop and deploy tools and services for our team to accelerate the production lifecycle and assessment of production readiness
  • Help lead team members in executing continuous integration and continuous delivery (CI/CD) activities to release code into a Production environment
  • Act as a consultant within the Science Organization on software engineering principles, code quality, and performance optimization techniques
  • Apply software engineering rigor and best practices to machine learning, including CI/CD and automation
  • Build model performance monitoring capabilities and data monitoring tools


  • MS Computer Science, Engineering, Technology, Mathematics, Statistics, or related field with 3+ years of industry experience or BS + 5 years' experience
  • Hands on coding experience with Python building end-to-end systems as an MLOps Engineer, Machine Learning Engineer, Software Engineer, or equivalent
  • Experience in ML model development, orchestration, deployment, monitoring, support and creating and maintaining deployment pipelines with CI/CD tools
  • Experience with cloud computing platforms like AWS, GCP, or other cloud providers developing with containers (e.g., Docker, Kubernetes) in cloud computing environments
  • Experience with database, such as SparkSQL, MongoDB, SQL, and SQLite


  • Exposure to deep learning approaches and modeling frameworks (Py Torch, TensorFlow, Keras, etc.)
  • Experience building ML web service, such as Flask, JavaScript, HTML, and Django.· Familiarity with Kubeflow or similar platforms like MLflow or SageMaker
  • Experience building and evaluating machine learning models
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Experience mentoring and teaching software development best practices to data scientists
  • Ability to translate complex technical concepts to collaborations, decision makers, and non-technical audiences

The ideal candidate will have a passion for generating new ideas, be a proactive and quick learner, and be able to demonstrate creativity and innovation.


San Diego, California