Blackline Systems Jobs (NOW HIRING)

Make Your Mark: We’re looking for a Senior AI/ML Engineer 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.

• Lead 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 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.
• Lead incident response and reliability strategies for ML/AI systems.
• Collaborate with development teams to integrate AI solutions into existing workflows and applications.
• Define and manage MCP Registry for agentic component onboarding, lifecycle versioning, and dependency governance.
• Build CI/CD pipelines automating LLM agent deployment, policy validation, and prompt evaluation of workflows.
• Develop and operationalize experimentation frameworks for agent evaluations, scenario regression, and performance analytics.
• Implement logging, metering, and auditing for agent behavior, function calls, and compliance alignment.
• Create scalable observability systems: tracking conversation outcomes, factual accuracy, latency, escalation patterns, and safety events.
• Architect end‑to‑end guardrails for AI agents including prompt injection protection, identity‑aware routing, and tool usage authorization.
• Collaborate cross‑functionally to standardize authentication, authorization, and session governance for multi‑agent runtimes.
• Architect and standardize model registries and feature stores to support version tracking, lineage, and reproducibility across environments.
• Lead the deployment of machine learning models into production environments, ensuring scalability, reliability, and efficiency.
• Collaborate with software engineers to integrate machine learning models into existing applications and systems.
• Implement and maintain APIs for model inference.
• Design and manage training infrastructure including distributed training orchestration, GPU/TPU resource allocation, and automatic scaling.
• Implement CI/CD for model workflows using pipelines integrated with model validation, bias checks, and rollback automation.
• Build standardized experimentation frameworks for reproducible training, tuning, and deployment cycles (MLflow, W&B, Kubeflow).
• Manage and optimize the infrastructure required for machine learning operations in the cloud.
• Collaborate with other teams to ensure availability, security, and performance of machine learning systems.
• Implement robust monitoring solutions for deployed machine learning models to detect issues and ensure performance.
• Collaborate with data scientists and engineers to address and resolve model performance and data quality issues.
• Conduct regular system maintenance, updates, and optimizations to ensure optimal performance of machine learning solutions.
• Develop and maintain automation scripts and tools for managing machine learning workflows.
• Implement orchestration systems to streamline the end‑to‑end machine learning lifecycle, from data preparation to model deployment.
• Facilitate the transition of machine learning models from research to production, ensuring scalability and efficiency.
• Identify and implement optimizations to enhance the performance and efficiency of machine learning models in production.
• Conduct performance analysis and implement improvements based on resource utilization.
• Implement security measures to protect machine learning systems and data.
• Ensure compliance with regulatory requirements and industry standards related to machine learning and data privacy.
• Integrate audit controls, metadata storage, and lineage tracking across ML and AI workflows.
• Ensure complete monitoring and feedback loops including event logs, evaluations, and automated retraining triggers.
• Enforce secure deployment patterns with Infrastructure‑as‑Code and cloud‑native secrets management.
• Define SLAs, error budgets, and compliance reporting mechanisms for ML and AI systems.

• Partner with data science, security, and product teams to set evaluation and governance standards (Guardrails, Bias, Drift, Latency SLAs).
• Mentor senior engineers and lead design reviews for ML pipelines, model registries, and agentic runtime environments.
• Aspire to lead AI system deployment and integration across platforms.
• Architect end‑to‑end guardrails protecting AI agents.
• Standardize authentication, authorization, and session governance for multi‑agent runtimes.
• Take part in building scalable observability and metering systems for agent behavior.

• 3+ years of experience with programming skills in 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 GCP, AWS, or Azure.
• Deep familiarity with LangChain, LangGraph, ADK, or similar agentic system runtimes.
• Strong competencies in CI/CD, IaC, and DevSecOps pipelines integrating testing, compliance, and deployment automation.
• Hands‑on with observability stacks (Prometheus, Grafana, New Relic) 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 (Docker, Kubernetes).

• Operations and infrastructure: scripting languages (Bash, Python) for automation.
• Experience with workflow orchestration tools and managing cloud‑based infrastructure.
• Expertise in DevOps practices and tools for automated deployment.
• Understanding of network configurations and security protocols.
• Strong critical‑thinking skills to evaluate models and propose innovative solutions.
• Adaptability and learning agility in a fast‑paced, evolving environment.

Salary Range: $145,000.00–$182,000.00. Pay transparency statement: Placement within this range depends on prior experience, skill set, and geographic location. Compensation may include a variable incentive component and a base salary, with possible split models (e.g., 50/50, 70/30, 60/40). BlackLine is committed to pay transparency and clear compensation expectations.

We are committed to creating an inclusive and accessible experience for all candidates. If you require a reasonable accommodation to better enable your success during the application or interview process, please let us know.

BlackLine is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity or expression, race, ethnicity, age, religious creed, national origin, physical or mental disability, ancestry, color, marital status, sexual orientation, military or veteran status, status as a victim of domestic violence, sexual assault or stalking, medical condition, genetic information, or any other protected class or category recognized by applicable equal employment opportunity or other similar laws.