Principal Machine Learning Engineer, Business Process Optimization

Description

The Business Process Optimize team sits inside Admin Central, which is building a single, unified experience for administrators across the company's platform in place of today's scattered tools and disconnected AI.

The team runs the underlying platform infrastructure: mission-critical distributed systems, high-availability usage pipelines, and event-driven data flows across global product regions that keep enterprise billing accurate at scale.

The company is a Fortune 500 firm and a leading AI platform for managing people, money, and agents.

The role is hands-on and sits where machine learning, event-driven data systems, and enterprise platform engineering meet. You will turn AI research into production systems that handle tens of millions of daily requests for more than 100 Tier-0 enterprise tenants.

You will design the ML infrastructure, multi-agent orchestration layers, and high-availability usage pipelines that support enterprise decision-making, usage tracking, and billing integrity across international regions.

Responsibilities

• Design, build, and maintain agentic capabilities such as tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
• Build secure, high-throughput REST services and event-driven data pipelines in Python on Kubernetes and cloud-native technology.
• Design multi-tenant runtimes that deliver fast, low-latency inference to millions of users worldwide.
• Set up end-to-end MLOps standards covering CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy and anonymization guardrails so systems stay explainable, secure, and resilient in production.
• Work closely with software engineers, product managers, data engineering, SRE, and legal and compliance teams to turn unclear business needs into scalable technical designs and long-term data governance strategies.

Requirements

• 12+ years in ML development and R&D leadership, including 8+ years with advanced ML methods, distributed frameworks, and cloud-based platforms, designing complex, scalable, production-grade ML systems.
• 6+ years building and hosting scalable ML services, microservices, and high-throughput data pipelines in production, using Java, Go, Scala, or Python.
• 4+ years working with LLMs, generative AI, RAG pipelines, and autonomous agent frameworks in real-world enterprise applications.
• Expert in high-throughput streaming with Kafka, real-time feature extraction with Spark or Flink, and distributed columnar storage with ClickHouse or Iceberg for large-scale ML pipelines.
• Deep knowledge of deep learning, LLM orchestration, RAG, and agentic systems, including MCP integrations and tool-use frameworks, applied to multi-agent coordination, intent classification, and workflow optimization.
• Proven ability to lead exploratory data analysis on multi-tenant agentic workflows to shape data strategy, plus strong skill with high-dimensional embeddings, vector database management, and data isolation.
• Proven ability to lead model building, fine-tuning, and deployment of domain-specific foundation models, including Kubernetes and Docker deployment, automated CI/CD retraining, and managing drift and latency at scale.
• Mastery of Python and the ML ecosystem (PyTorch, TensorFlow, LangChain, LangSmith, LlamaIndex, NumPy, Pandas, Scikit-learn), with solid experience running production systems on PostgreSQL, Redis, Kafka, and AWS, GCP, or Azure.
• Exceptional collaboration and mentoring skills. You will lead cross-functional pods, drive sprint planning, and explain technical and governance decisions to executives.
• A bachelor's degree in computer science, data science, statistics, mathematics, engineering, or a related field, or equivalent experience. A master's or PhD is strongly preferred.

Conditions

Base salary for the primary location, Pleasanton, California, USA, is $228,000 USD to $342,000 USD. Additional US locations range from $190,600 USD to $342,000 USD. Pay varies by location and is set by geography, experience, skills, job duties, and business need.

The role may be eligible for the company Bonus Plan or a role-specific commission or bonus, plus annual refresh stock grants. The recruiter can share more detail during hiring.

Flex work means at least 50% of your time each quarter is spent in the office or in the field with customers, prospects, and partners, depending on the role. There is no fixed number of office days, so you can build a schedule around your work and personal needs. Remote home office roles can also join in-office moments that matter.

The company will consider qualified applicants with arrest and conviction records, in line with applicable Fair Chance law.

The company is an equal opportunity employer that includes individuals with disabilities and protected veterans.

Benefits

• Benefits details are available from the company on request.