Senior Staff Machine Learning Engineer

Description

The AI Engineering and Delivery group builds agentic AI and enterprise-scale search systems that run as core platform infrastructure, with a focus on robustness, performance, safety, and customer impact at scale.

The emerging tech team is a small senior group within that organization. It turns early AI bets into strategic capability and works on fast-moving agentic AI features that the platform and products will rely on.

You will design and build production agentic systems across the company's platform. These are autonomous agents that reason over enterprise data, take action across workflows, and stay safe at Fortune 500 scale.

Core focus areas include agent architecture, grounding agents in the company's data layer, governance, retrieval quality, model integration, and technical leadership.

The company's agent work already runs in production: AI specialists resolve cases across IT, CRM, HR, and security; an open integration layer via MCP exposes the company's system of action to external agents, with Anthropic's Claude Cowork as the first design partner; a joint project with NVIDIA brings governed autonomous desktop agents to production; agent building is available inside Cursor, Claude Code, and GitHub Copilot; and a control tower offers kill-switch controls and cross-vendor agent governance.

The platform spans over 100B workflows and 6.5T transactions a year, and serves 85% of the Fortune 500. The team is building the layer that connects Sense (any data), Decide (any AI model), Act (any workflow), and Govern (identity and governance).

You will work across several problem areas at the edge of the company's work and apply new model capabilities to real customer problems on short timelines.

Responsibilities

• Design and ship multi-agent systems with orchestration, tool use, planning loops, memory, and failure recovery that run reliably in production.
• Build agents that use the company's CMDB, workflow data fabric, and knowledge graph to make decisions with context that frontier models lack alone.
• Own guardrails, including observability, human-in-the-loop controls, and compliance infrastructure that make autonomous systems safe to deploy at scale.
• Work with the search team to keep agents grounded in accurate, low-latency retrieval, covering RAG pipelines, hybrid search, re-ranking, and evaluation.
• Integrate frontier models from Anthropic, Google, and OpenAI into the Sense, Decide, Act, Govern architecture, and weigh cost, latency, and capability for production use.
• Set the architectural patterns the group follows, make the hard design calls, run design reviews, and raise the standard for agent design and production AI practice across engineers and principals.
• Design scalable, robust architectures for deployment across hyperscalers and the company's own infrastructure.

Requirements

• At least 6 years of software engineering, with strong fundamentals in data structures, algorithms, and distributed systems.
• Hands-on experience designing, shipping, and operating production agentic systems, including multi-agent orchestration, tool calling, planning loops, memory, and failure recovery.
• Production-grade Python.
• Experience with frontier AI SDKs from Anthropic, Google, or OpenAI, including prompt engineering, structured outputs, and model evaluation in production.
• Familiarity with production RAG and retrieval, including vector stores, hybrid search, and retrieval evaluation metrics.
• A record of technical leadership, including owning architecture, raising code quality, and mentoring engineers on production AI practice.
• Nice to Have: Deeper work in search and retrieval at scale, or in MLOps and model observability.
• Nice to Have: Published work or open-source contributions in agentic systems or retrieval.
• Nice to Have: Exposure to LLM fine-tuning or inference optimization in production.
• Systems experience in Go, Java, or C++ is a plus.

Conditions

Base pay for this role in this location is $201,300 - $352,300, plus equity where applicable, variable or incentive pay, and benefits.

The base pay shown is a guideline. Total pay depends on qualifications, skill level, competencies, and work location, and pay is based on the geographic location where the role sits and may change.

Work persona is flexible, remote, or required in office, assigned by the nature of the work and the work location. The company may use a third-party service to check the distance from your home to the nearest office.

Benefits

• Health plans, including flexible spending accounts.
• 401(k) plan with company match.
• Employee stock purchase plan (ESPP).
• Matching donations.
• Flexible time away and family leave programs.