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About This Role
Job description
Company and benefits
Job ID
SRSTA019372
Employment Type
Regular
Work Style
hybrid
Location
Seattle,WA,United States
Role
Sr. Staff Software Engineer (Agentic AI)
Why UKG:
At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform. Helping people get paid, grow in their careers, and shape the future of their industries. That’s what we do.
We never stop learning. We never stop challenging the norm. We push for better, and we celebrate the wins along the way. Here, you’ll get flexibility that’s real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters—and so do you.
\*\*\*UKG is unable to offer sponsorship for this position.\*\*\*
Sr. Staff Software Engineer \- Agentic Acceleration Group
We are seeking an exceptional Sr. Staff Software Engineer to define and drive our enterprise\-wide agentic AI strategy on Google's Agent Development Kit (ADK) and other agentic platforms. You will shape the future of intelligent automation across UKG's product portfolio, influence technical direction across multiple engineering organizations, and establish UKG as a thought leader in enterprise agentic AI. As a Sr. Staff Engineer, you will set technical vision, architect organization\-wide systems, drive transformational change, and elevate engineering excellence company\-wide.
Responsibilities:
- Strategic Technical Leadership: Define enterprise\-wide technical strategy for agentic AI across UKG. Set long\-term vision for how agents transform UKG's HCM platform. Influence product roadmaps and business strategy through technical insight. Drive division\-level or company\-level objectives.
- Enterprise Architecture: Architect organization\-wide agentic AI capabilities on Google ADK and other platforms. Design foundational frameworks, orchestration patterns, and platform capabilities that serve multiple product teams. Set architectural standards and best practices for agent development across the organization.
- Cross\-Organizational Influence: Drive technical decisions across multiple teams and divisions. Set standards for agent development, tool design, safety frameworks, and operational excellence. Shape engineering culture and technical direction at scale.
- Advanced Agent Systems: Pioneer sophisticated multi\-agent architectures including hierarchical systems, swarm intelligence, adversarial networks, and hybrid human\-agent workflows. Push boundaries of what's possible with agent orchestration. Demonstrate engineering excellence through industry\-leading implementations.
- Innovation \& Research: Stay at the forefront of agentic AI research. Evaluate emerging platforms and techniques. Prototype breakthrough capabilities. Drive adoption of transformative technologies. Contribute to industry knowledge through publications, open source, or conference presentations.
- Agent Safety \& Governance: Establish organization\-wide frameworks for agent safety, governance, and responsible AI. Design policy engines and guardrail architectures that scale across all systems. Influence company\-wide AI safety policy and regulatory compliance standards.
- Platform Integration: Design integrations between agents and UKG's entire enterprise ecosystem. Build strategic tools, APIs, and MCP servers that unlock capabilities across products. Define integration patterns that scale across teams and product lines.
- Service Excellence: Define service health, quality, and operational standards for all agentic systems. Establish monitoring, observability, and incident response frameworks. Set the bar for production excellence, reliability, and cost efficiency.
- Mentorship \& Talent Development: Mentor senior engineers, lead engineers, and engineering managers. Raise the technical bar through code reviews, design reviews, and architectural guidance. Build the next generation of technical leaders. Recognized as go\-to expert and trusted advisor.
- Thought Leadership: Represent UKG externally as a technical expert. Publish articles, present at conferences, contribute to open source, and engage with the AI engineering community. Establish UKG's technical reputation and attract top talent.
- Strategic Partnerships: Engage with technology partners (Google, Anthropic) to influence platform roadmaps. Represent UKG in technical advisory relationships. Leverage partnerships to drive internal innovation.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience).
- 10\+ years of professional software development experience, with 3\+ years building agentic AI systems or LLM\-powered applications in production at scale.
- Deep expertise in Python, Java, C\#, JavaScript, or TypeScript, with track record of setting coding standards and best practices.
- Proven experience architecting and building production agents using Google ADK, Anthropic Claude SDK, LangChain, LangGraph, or similar platforms.
- Expert\-level understanding of prompt engineering, context engineering, and advanced agent design patterns (ReAct, Chain\-of\-Thought, constitutional AI, multi\-agent collaboration).
- Track record of influencing technical direction across multiple teams or organizations. Demonstrated ability to set architectural standards and drive adoption.
- Proven ability to lead highly complex, cross\-functional initiatives with significant business impact.
- Deep expertise with GCP (Vertex AI, Google ADK), Azure (Azure OpenAI), or AWS (Bedrock), and modern DevOps practices.
- Exceptional problem\-solving skills, systems thinking, and ability to navigate complex technical and organizational challenges.
- Outstanding leadership, communication, and influence skills. Ability to drive technical decisions across senior stakeholders and executive audiences.
- Demonstrated track record of mentoring senior engineers and raising the technical bar across organizations.
Company Overview:
UKG is the Workforce Operating Platform that puts workforce understanding to work. With the world's largest collection of workforce insights, and people\-first AI, our ability to reveal unseen ways to build trust, amplify productivity, and empower talent, is unmatched. It's this expertise that equips our customers with the intelligence to solve any challenge in any industry — because great organizations know their workforce is their competitive edge. Learn more at ukg.com.
Equal Opportunity Employer
UKG is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, disability, religion, sex, age, national origin, veteran status, genetic information, and other legally protected categories.
View The EEO Know Your Rights poster
UKG participates in E\-Verify.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Disability Accommodation in the Application and Interview Process
For individuals with disabilities that need additional assistance at any point in the application and interview process, please email UKGCareers@ukg.com.
The pay range for this position is $145,600 to $240,695\. The actual base pay offered may vary depending on skills, experience, job\-related knowledge and work location. In addition to base pay, employees may be eligible to participate in a performance\-based bonus plan and to receive restricted stock unit awards as part of total compensation. Learn more about UKG’s benefits and rewards at https://www.ukg.com/about\-us/careers/benefits
NOTICE ON HIRING SCAMS
UKG will never ask you for a copy of your driver’s license, social security card, or passport during a job inter
ABOUT OUR JOB DESCRIPTIONS
All job descriptions are written to accurately reflect the open job and include general work responsibilities. They do not present a comprehensive, detailed inventory of all duties, responsibilities, and qualifications required for the job. Management reserves the right to revise the job or require that other or different tasks be performed if or when circumstances change.
Salary Context
This $145K-$240K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At UKG, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($193K) sits 12% below the category median. Disclosed range: $145K to $240K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
UKG AI Hiring
UKG has 2 open AI roles right now. They're hiring across AI Software Engineer. Based in Seattle, WA, US. Compensation range: $214K - $240K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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