Senior Machine Learning Platform Engineer
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Anywhere in the U.S.Full-TimeSenior
Salary160,000 - 210,000 USD per year
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Job Details
- Experience
- 5+ years of experience in Platform Engineering, 2+ years of experience owning ML systems end-to-end
- Required Skills
- DockerPythonElasticSearchKafkaKubernetesC++GoRedisRust
Requirements
- 5+ years of experience in Platform Engineering, with a proven track record of deploying and maintaining a scalable ML platform in high-traffic production environments.
- 2+ years of experience owning ML systems end-to-end in production, including on-call and incident response.
- Experience with Real-Time Data, proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services.
- Deep MLOps expertise building a platform for managing the full ML lifecycle (training, deploying, monitoring).
- Experience using MLOps tools like SageMaker, VertexAI, Vector DBs, Graph Databases.
- Managing and scaling caches like Redis or Elasticsearch.
- Proficient with Containerization, Docker, Kubernetes, and cluster-level management.
- Expert in Python.
- Proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers.
Responsibilities
- Design and build the end-to-end machine learning infrastructure.
- Setup platform for transitioning experimental Data Science models into robust, high-availability production services.
- Build automation for deploying low-latency services to serve model inferences in milliseconds.
- Lead the creation and optimization of a centralized feature store.
- Work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement.
- Champion best practices for model deployment, monitoring, and CI/CD for ML.
- Implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.
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