Sr. Machine Learning Engineer
United StatesFull-TimeSenior
Salary164000 - 194000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSDockerPythonSQLDynamoDBETLKafkaKubernetesPyTorchSnowflakeTensorflowTerraformDatabricksCloudFormationscikit-learn
Requirements
- 5+ years of experience in Machine Learning Engineering, with strong focus on production systems and data engineering.
- Strong expertise in AWS cloud services (e.g., SageMaker, DynamoDB) and infrastructure-as-code tools such as Terraform, CDK, or CloudFormation.
- Deep experience with containerization and orchestration technologies including Docker and Kubernetes.
- Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Advanced knowledge of ETL pipelines, database systems, and large-scale data processing.
- Experience with big data and distributed systems such as Snowflake, Databricks, or Kafka is highly desirable.
- Strong understanding of SQL and data modeling for analytical and operational use cases.
- Proven ability to collaborate across data science, engineering, and product teams.
- Strong problem-solving skills with a focus on scalability, reliability, and performance.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
Responsibilities
- Architect and own the end-to-end machine learning infrastructure, ensuring scalable and production-ready systems.
- Partner with data science teams to productionize models and transition algorithms from research to real-world applications.
- Design, build, and maintain feature stores (offline and online) to support real-time and batch model inference.
- Develop and optimize ML pipelines and data workflows using modern cloud-native architectures.
- Collaborate with data engineering teams to enhance data lake, ETL, and streaming data infrastructure.
- Lead system monitoring, observability, and performance optimization for production ML models.
- Contribute to architectural decisions and define best practices for scalable data and ML systems.
- Ensure reliability, fault tolerance, and efficiency across all machine learning services in production.
- Support cross-functional collaboration by translating data science needs into scalable engineering solutions.
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