Description

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

Requirements

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

Preferred:

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.