Software Engineer Ii, ML Ops

Whoop Whoop · Consumer · Boston, MA · Machine Learning & Research

Software Engineer II, ML Ops role at WHOOP focused on building and optimizing ML cloud infrastructure to support Data Science and AI teams in productionalizing machine learning models. Responsibilities include designing and maintaining cloud infrastructure, implementing CI/CD pipelines for ML models, developing integration components, and leveraging AWS services for scalable and cost-effective ML/AI workloads.

What you'd actually do

  1. Design, develop, and maintain cloud-based infrastructure to support the deployment and scaling of machine learning models. Implement automated pipelines for continuous integration and continuous deployment (CI/CD) of ML models, ensuring seamless transitions from development to production environments.
  2. Collaborate closely with Data Scientists and AI teams to understand model requirements and facilitate the transition from prototype to production.
  3. Develop APIs, microservices, and other components necessary to integrate ML models into existing systems, enabling real-time inference and decision-making.
  4. Leverage cloud services to optimize the deployment and performance of machine learning models and associated infrastructure. Utilize services such as AWS SageMaker, Lambda, and ECS to build scalable, cost-effective solutions that support real-time ML/AI workloads.
  5. Support AI teams by troubleshooting and resolving technical challenges related to model deployment and performance in production.

Skills

Required

  • Cloud-based infrastructure development
  • ML model deployment and scaling
  • CI/CD for ML models
  • API and microservice development
  • Integration of ML models into existing systems
  • Real-time inference
  • AWS SageMaker
  • AWS Lambda
  • AWS ECS
  • Troubleshooting ML deployment issues

Nice to have

  • Experience with MLOps tools and best practices
  • Familiarity with data science workflows
  • Understanding of machine learning concepts

What the JD emphasized

  • productionalization of machine learning models
  • production
  • production
  • production
  • production

Other signals

  • MLOps
  • ML cloud infrastructure
  • productionalization of machine learning models
  • CI/CD of ML models
  • APIs and microservices for ML integration
  • AWS SageMaker, Lambda, ECS