Sr. Machine Learning Software Engineer
New
Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range.Full-TimeSenior
Salary153000 - 179000 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSPythonJavaKubernetesMLFlowSnowflakeAirflowCI/CDTerraformDatabricksMLOps
Requirements
- 7+ years of experience in software engineering for ML production or ML platform delivery.
- Hands-on experience deploying ML models via APIs, batch pipelines, or streaming inference.
- Proficiency in Python (required), Java, or similar, with software engineering best practices for ML workflows.
- Experience with unit, integration, and pipeline-level testing for ML models, including data validation, correctness checks, and reproducibility.
- Familiarity with cloud platforms (AWS preferred: SageMaker, S3, EC2) and reproducible ML pipelines.
- Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms (SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows.
- Strong collaboration skills.
Responsibilities
- Collaborate with AI scientists to package and deploy ML models, ensuring reproducibility, versioning, and compliance.
- Build and maintain model serving infrastructure including monitoring, drift detection, automated retraining, and logging.
- Implement unit, integration, and system-level testing for ML models, covering data validation, model correctness, and deployment workflows.
- Develop and operate end-to-end ML pipelines: ingestion → preprocessing → feature engineering → evaluation → deployment → monitoring.
- Integrate CI/CD and MLOps practices for automated model builds, testing, and deployment.
- Identify and resolve workflow inefficiencies or gaps between research and production.
- Recommend and integrate frameworks, libraries, and infrastructure to improve pipeline efficiency, maintainability, and observability.
- Collaborate cross-functionally to ensure compliance with regulatory requirements (FDA/HIPAA) in production ML workflows.
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