By joining our team, you’ll be at the forefront of the AI transformation, backed by the global scale of ServiceNow and the agility of a high-growth company. We are looking for world-class talent to help us extend agentic AI to every employee across every corner of the business.
Come join us!
ServiceNow
It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.
The Role
We're building the runtime infrastructure that powers Moveworks' AI agents — the systems that orchestrate, execute, and deliver agent responses to millions of enterprise users in real time. This role sits where large language models meet distributed systems. You'll apply machine learning, LLM, and distributed systems expertise to make agents plan, reason, and use tools reliably, and you'll build the production systems that run those agents at enterprise scale.
Our AI agents can plan, execute multi-step workflows, call tools, wait on human input, and resume — all while maintaining correctness, observability, and low latency. The systems that make this possible are what you'll build and own.
What you get to do in this role
Agent orchestration engine — A state machine that manages long-running agent sessions, coordinating planning, execution, and user interaction across multiple LLM calls and tool invocations
Model routing and adaptation — Choose, route between, and fine-tune or distill LLMs for specific agent tasks. Meet enterprise requirements such as granular permissions, data freshness, explainability, and preserving general knowledge.
Distributed session management — Lease-based ownership using conditional writes, heartbeat protocols, and crash recovery via checkpointing
Event-driven message pipeline — FIFO queues for ordered delivery, consumers for event processing, and real-time streaming via gRPC and Socket.IO
Structured concurrency — Running multiple concurrent tasks per session with fail-fast semantics and graceful cancellation
Observability infrastructure — OpenTelemetry instrumentation, distributed trace context propagation across async boundaries, custom span lifecycle management for sessions that span minutes
Caching and state layers — Redis, DynamoDB KV stores with per-org/per-bot scoping, batch read optimization, and hot-reload configuration
Tool and agent connectivity for LLMs: Build MCP and agent-to-agent (A2A) capabilities, including tool discovery, namespacing, and tool selection over large tool catalogs, so the LLM picks and calls the right tool from thousands of candidates.
Token efficiency and context engineering — Lower the agent's cost and latency through prompt and context discipline, caching, speculative and parallel model-call scheduling, and reducing tokens spent across multi-step LLM reasoning chains.
Open-weights model adoption and evaluation — Quickly stand up newly released open-weights LLMs at different quantization levels, run them in a test environment, and benchmark their accuracy, latency and cost on agent tasks. Use the results to decide which models reach production.
To be successful in this role you have
You should have deep experience in at least 3 of these areas
Distributed systems: consistency models, idempotency, exactly-once delivery, distributed locking/leasing
Concurrent/async programming: Python asyncio, Go goroutines, structured concurrency, cancellation handling
Event-driven architectures: message queues (SQS, Kafka), pub/sub, backpressure, delivery guarantees
Database systems for infrastructure: DynamoDB (conditional writes, transactions), Redis (connection pooling, pub/sub)
Observability: OpenTelemetry, distributed tracing, span context propagation, Prometheus metrics
gRPC/protobuf: streaming RPCs, service interface design, error handling patterns
Machine learning: Large language models, natural language processing, LLM evaluation
Required
5+ years building production backend/infrastructure systems
Strong in Python or Go (ideally both)
Experience designing and operating systems that handle real traffic at scale
Hands-on experience building production systems that use LLMs or other ML models
Comfort with ambiguity — these are novel problems without textbook solutions
Relevant past experience
Building and shipping products on large language models with techniques such as RAG, chain-of-thought, self-reflection, tool and function calling, and agentic reasoning.
Training, fine-tuning, or evaluating machine learning models on large datasets in production, especially for NLP, retrieval, or ranking.
Improving relevance or quality through careful experimentation: designing metrics, building evaluation datasets, and developing new features and signals for ranking or model selection.
Research or applied work in machine learning, LLMs, agentic systems, information retrieval, or ranking and relevance.
Work Personas
We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.
Equal Opportunity Employer
ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.
Accommodations
We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.
Export Control Regulations
For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.
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