We are looking for a software engineer and technical lead with deep software design and architecture skills, excellent coding ability, and the technical judgment to build reliable production systems at enterprise scale.
ServiceNow's core platform is built on a powerful and highly scaled Java service that supports some of the world's largest enterprise workflows. In this team, we are extending that platform with modern Python and Java microservices that complement the main platform architecture and enable new AI-native capabilities. These services are designed to run in Kubernetes-based environments while integrating deeply with the broader ServiceNow platform.
We are focused on production-grade services that bring together enterprise data, AI model integrations, scalable APIs, evaluation workflows, and reliable deployment patterns. You will own the design, development, and operation of containerized backend services running in Kubernetes environments. You will apply strong distributed systems and production reliability judgment to build scalable, maintainable services, and you will lead technical design across ambiguous, high-impact problem spaces.
Experience working on AI, GenAI, ML-powered, or data-intensive products will help you be especially successful in this role.
- Designing, architecting, and implementing Java and Python microservices deployed on Kubernetes
- Designing APIs, service boundaries, data models, and integration patterns for AI-enabled products
- Building reliable distributed systems that handle concurrency, queueing, retries, fairness, backpressure, and failure recovery
- Integrating frontier AI SDKs such as Anthropic, Google, and OpenAI into production software systems
- Applying prompt engineering, structured outputs, model evaluation, and production observability to GenAI use cases
- Partnering with research, product, security, and infrastructure teams to ship enterprise-grade services
- Raising the engineering bar through architecture reviews, code reviews, mentoring, and technical direction
- 8+ years of professional software engineering experience, with strong fundamentals in data structures, algorithms, distributed systems, APIs, and backend service design
- Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving (including using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact)
- Demonstrated experience as a technical lead, Staff Engineer, or equivalent senior individual contributor responsible for architecture, technical direction, code quality, and mentoring
- Strong backend engineering skills in Java, Python, or equivalent with hands-on experience designing, building, and operating production services
- Hands-on experience building or operating containerized applications deployed in Kubernetes, OpenShift, or similar orchestration environments
- Experience designing scalable APIs, asynchronous processing systems, queues, event-driven services, or data-processing pipelines
- Familiarity with cloud-native infrastructure, service observability, logging, monitoring, reliability engineering, and production troubleshooting
- Ability to make pragmatic architecture decisions across performance, reliability, security, maintainability, and delivery speed
- Ability to lead other engineers through code reviews, design reviews, and technical mentorship
- Strong communication skills and the ability to partner across product, research, engineering, infrastructure, and security
- Prior experience working on AI/ML products, collaborating with research teams, or translating advanced AI/ML capabilities into production software
- Knowledge of NLP, search, and knowledge extraction
- Experience with multi-modal systems such as document ingestion and processing, image processing, and voice
- Working experience with frontier AI SDKs such as Anthropic, Google, or OpenAI
- Familiarity with prompt engineering, structured outputs, tool calling, agentic design patterns, Model Context Protocol, or AI-assisted development workflows
- Experience with observability tools such as Prometheus, Grafana, Instana, or equivalent monitoring/logging platforms
- Knowledge of MLOps or applying machine learning models to production use cases
- Experience with infrastructure backends such as PostgreSQL, Redis, Kafka, RabbitMQ, S3-compatible object storage, or equivalent technologies
- Published work, patents, conference papers, or open-source contributions related to AI systems, knowledge systems, search, retrieval, or large-scale data processing.
Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
For positions in this location, we offer a base pay of $190,900 - $334,100, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location.
We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs.
Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
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To be successful in this role, you have:
- 8+ years of professional software engineering experience, with strong fundamentals in data structures, algorithms, distributed systems, APIs, and backend service design
- Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving (including using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact)
- Demonstrated experience as a technical lead, Staff Engineer, or equivalent senior individual contributor responsible for architecture, technical direction, code quality, and mentoring
- Strong backend engineering skills in Java, Python, or equivalent with hands-on experience designing, building, and operating production services
- Hands-on experience building or operating containerized applications deployed in Kubernetes, OpenShift, or similar orchestration environments
- Experience designing scalable APIs, asynchronous processing systems, queues, event-driven services, or data-processing pipelines
- Familiarity with cloud-native infrastructure, service observability, logging, monitoring, reliability engineering, and production troubleshooting
- Ability to make pragmatic architecture decisions across performance, reliability, security, maintainability, and delivery speed
- Ability to lead other engineers through code reviews, design reviews, and technical mentorship
- Strong communication skills and the ability to partner across product, research, engineering, infrastructure, and security
- Prior experience working on AI/ML products, collaborating with research teams, or translating advanced AI/ML capabilities into production software
- Knowledge of NLP, search, and knowledge extraction
- Experience with multi-modal systems such as document ingestion and processing, image processing, and voice
- Working experience with frontier AI SDKs such as Anthropic, Google, or OpenAI
- Familiarity with prompt engineering, structured outputs, tool calling, agentic design patterns, Model Context Protocol, or AI-assisted development workflows
- Experience with observability tools such as Prometheus, Grafana, Instana, or equivalent monitoring/logging platforms
- Knowledge of MLOps or applying machine learning models to production use cases
- Experience with infrastructure backends such as PostgreSQL, Redis, Kafka, RabbitMQ, S3-compatible object storage, or equivalent technologies
- Published work, patents, conference papers, or open-source contributions related to AI systems, knowledge systems, search, retrieval, or large-scale data processing.
Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
For positions in this location, we offer a base pay of $190,900 - $334,100, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location.
We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs.
Compensation is based on the geographic location in which the role is located and is subject to change based on work location. We are looking for a software engineer and technical lead with deep software design and architecture skills, excellent coding ability, and the technical judgment to build reliable production systems at enterprise scale.
ServiceNow's core platform is built on a powerful and highly scaled Java service that supports some of the world's largest enterprise workflows. In this team, we are extending that platform with modern Python and Java microservices that complement the main platform architecture and enable new AI-native capabilities. These services are designed to run in Kubernetes-based environments while integrating deeply with the broader ServiceNow platform.
We are focused on production-grade services that bring together enterprise data, AI model integrations, scalable APIs, evaluation workflows, and reliable deployment patterns. You will own the design, development, and operation of containerized backend services running in Kubernetes environments. You will apply strong distributed systems and production reliability judgment to build scalable, maintainable services, and you will lead technical design across ambiguous, high-impact problem spaces.
Experience working on AI, GenAI, ML-powered, or data-intensive products will help you be especially successful in this role.
- Designing, architecting, and implementing Java and Python microservices deployed on Kubernetes
- Designing APIs, service boundaries, data models, and integration patterns for AI-enabled products
- Building reliable distributed systems that handle concurrency, queueing, retries, fairness, backpressure, and failure recovery
- Integrating frontier AI SDKs such as Anthropic, Google, and OpenAI into production software systems
- Applying prompt engineering, structured outputs, model evaluation, and production observability to GenAI use cases
- Partnering with research, product, security, and infrastructure teams to ship enterprise-grade services
- Raising the engineering bar through architecture reviews, code reviews, mentoring, and technical direction