Senior AI/ML Engineer - Scalable Data & LLM Pipelines

BlackLine; Pleasanton (CA); On-site; USD 145,000 - 182,000; Full time

Benefits offered by this job

Incentive programs

Benefits & wellness

Job summary

BlackLine is seeking a Senior AI/ML Engineer to design, build, and optimize data pipelines powering our next‑generation AI‑driven accounting agents. You’ll lead scalable, high‑performance data infrastructure and collaborate closely with data science, security, and product teams.

You will lead PySpark ETL development, manage integrations via APIs (FiveTran, Plaid, internal connectors), monitor reliability, automate testing, and push for scaling improvements across ML systems and model deployment.

Qualifications

• 3+ years of programming experience in Python, Java, or Scala.
• Experience with ML frameworks and orchestration tools.
• Proficient in containerization (Docker, Kubernetes) and CI/CD.
• Familiarity with monitoring, logging, and model deployment workflows.

Responsibilities

• Design, build, and optimize data pipelines for AI-powered agents.
• Lead PySpark ETL development with high data quality and performance.
• Manage API integrations to client data sources (FiveTran, Plaid, internal connectors).
• Ensure reliability, testing, and data accuracy; improve performance for large datasets.
• Collaborate across data science, security, and product teams on governance and deployment.

Skills

Python/Java/Scala

ML frameworks

CI/CD

Docker/Kubernetes

Observability

LangChain

Tools

TensorFlow

PyTorch

scikit-learn

Airflow

Kubeflow

Vertex AI

MLflow

FiveTran

Plaid

Job description

BlackLine is seeking a Senior AI/ML Engineer to design, build, and optimize data pipelines powering our next‑generation AI‑driven accounting agents. You’ll lead scalable, high‑performance data infrastructure and collaborate closely with data science, security, and product teams.

You will lead PySpark ETL development, manage integrations via APIs (FiveTran, Plaid, internal connectors), monitor reliability, automate testing, and push for scaling improvements across ML systems and model deployment.