Make Your Mark:
We're looking for a Software Engineer, AI/ML Ops to design, build, and optimize data pipelines that power our next-generation AI-driven accounting agents. You'll lead the development of scalable, high-performance data infrastructure while collaborating closely across teams. Responsibilities: - Data pipeline development: Build and maintain PySpark ETL pipelines with high data quality and performance - Manage integrations: Establish robust connections to client data sources via APIs and tools like FiveTran, Plaid, and BlackLine's own internal connector ecosystem - Ensure reliability: Monitor pipeline performance, automate testing, and validate data accuracy - Optimize for scale: Implement performance improvements (e.g., CDC mechanisms, indexing strategies) for large-scale datasets - Collaborate & innovate: Work with business stakeholders to refine data requirements and integrate cutting-edge AI and big data technologies

You'll Get To:

We're looking for a Software Engineer, AI/ML Ops to design, build, and optimize data pipelines that power our next-generation AI-driven accounting agents. You'll lead the development of scalable, high-performance data infrastructure while collaborating closely across teams. Responsibilities:

• Data pipeline development: Build and maintain PySpark ETL pipelines with high data quality and performance
• Manage integrations: Establish robust connections to client data sources via APIs and tools like FiveTran, Plaid, and BlackLine's own internal connector ecosystem
• Ensure reliability: Monitor pipeline performance, automate testing, and validate data accuracy
• Optimize for scale: Implement performance improvements (e.g., CDC mechanisms, indexing strategies) for large-scale datasets
• Collaborate & innovate: Work with business stakeholders to refine data requirements and integrate cutting-edge AI and big data technologies

What You'll Bring:

• 2+ years of experience with programming skills in languages such as Python, Java, or Scala.
• Expertise in ML frameworks (TensorFlow, PyTorch, scikit-learn) and orchestration tools (Airflow, Kubeflow, Vertex AI, MLflow).
• Proven experience operating production pipelines for ML and LLM-based systems across cloud ecosystems (GCP, AWS, Azure).
• Deep familiarity with LangChain, LangGraph, ADK or similar agentic system runtime management.
• Strong competencies in CI/CD, IaC, and DevSecOps pipelines integrating testing, compliance, and deployment automation.
• Hands-on with observability stacks (Prometheus, Grafana, Newrelic) for model and agent performance tracking.
• Understanding of governance frameworks for Responsible AI, auditability, and cost metering across training and inference workloads.
• Proficiency in containerization technologies (e.g., Docker, Kubernetes).

We're Even More Excited If You Have:
Operations and Infrastructure:

• Proficient in scripting languages (e.g., Bash, python) for automation.
• Experience with workflow orchestration tools (e.g., Apache Airflow).
• Expertise in managing and optimizing cloud-based infrastructure.
• Familiarity with DevOps practices and tools for automated deployment.
• Understanding of network configurations and security protocols. Problem-solving and Critical Thinking:
• Ability to define problems, collect and analyze data, and propose innovative solutions. Strong critical thinking skills to evaluate models, identify limitations, and Adaptability and Learning Agility:
• Comfortable working in a fast-paced, rapidly evolving environment. Proactive in staying up to date with the latest trends, techniques, and technologies in AI/data science

Salary Range:
$145,000.00-$182,000.00

Pay Transparency Statement:

Placement within this range depends upon several factors, including the applicant's prior relevant job experience, skill set, and geographic location.

In addition to base pay, BlackLine also offers short-term and long-term incentive programs, based on eligibility, along with a robust offering of benefit and wellness plans.

We are committed to pay transparency and ensuring candidates have clear information about compensation expectations. For roles that include variable incentive components such as an Incentive Compensation Plan (ICP) or On-Target Earnings (OTE), the compensation structure may follow a split model - for example, a 50/50, 70/30, or 60/40 ratio between base salary and variable incentive.