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. About the Role We are seeking a Principal Cloud Platform Software Engineer in Enterprise Solutions and Experience (ESE) org to lead the architecture and development of a cloud-native onboarding platform that enables engineering teams to seamlessly deploy and operate services across multi-cloud environments including Google Cloud Platform (GCP), Microsoft Azure, and AWS. This role will drive the design of scalable SaaS platform services built on microservices architecture, Kubernetes, and modern DevOps practices, enabling standardized cloud onboarding, automation, and governance across multiple teams. As a Principal Engineer, you will act as a technical leader and architect, influencing platform strategy, mentoring engineers, and ensuring engineering excellence across distributed cloud systems. Key Responsibilities Architecture & Platform Strategy Lead the architecture and design of a cloud onboarding platform that enables teams to provision and manage resources across AWS, Azure, and GCP. Define cloud-native architectural patterns using microservices, APIs, event-driven architectures, and containerized workloads. Establish reference architectures, design standards, and platform capabilities that support scalable SaaS services. Ensure systems are designed for high availability, scalability, security, and resilience. Platform Development Design and build highly scalable backend services and APIs supporting cloud onboarding workflows and automation. Develop microservices deployed in Kubernetes environments using modern programming languages such as Java, Go, or Python. Build reusable platform components enabling self-service cloud onboarding for engineering teams. Integrate platform services with cloud-native capabilities including identity, networking, compute, and storage services. Multi-Cloud Platform Engineering Design and implement solutions supporting multi-cloud environments (AWS, Azure, GCP). Develop automation frameworks for infrastructure provisioning and lifecycle management. Implement Infrastructure as Code (Terraform or similar tools) to enable repeatable and secure cloud provisioning. Create abstraction layers and platform tooling that simplify multi-cloud operations and governance. DevOps & Operational Excellence Build and maintain CI/CD pipelines and automated deployment frameworks. Implement observability, monitoring, and logging solutions to ensure system reliability and performance. Drive site reliability engineering practices, including SLAs, SLOs, and incident response processes. Participate in production support, troubleshooting, and root cause analysis to continuously improve system stability. Technical Leadership Serve as a technical leader across engineering teams, influencing architecture and engineering practices. Mentor engineers on cloud-native development, distributed systems, and microservices best practices. Partner with product managers, architects, DevOps teams, and security teams to drive platform strategy and adoption. Lead design reviews and guide the long-term evolution of the platform. Minimum Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience. 10+ years of software development experience designing and building scalable distributed systems. Deep experience with microservices architecture and cloud-native application development. Strong experience working with public cloud platforms (AWS, Azure, GCP). Hands-on experience with Kubernetes and containerized application development. Strong backend development experience using Java, Go, Python, or similar languages. Experience implementing Infrastructure as Code (Terraform or similar). Experience designing large-scale distributed SaaS platforms. Strong understanding of cloud security, networking, and identity management. Experience implementing CI/CD pipelines and DevOps automation. Preferred Qualifications Experience building internal developer platforms or cloud onboarding systems. Experience with event-driven architectures and messaging systems (Kafka, Pub/Sub, SNS/SQS, Event Hub). Experience with service mesh technologies such as Istio or Linkerd. Experience implementing GitOps practices. Experience supporting multi-cloud architectures in enterprise environments. Strong experience working in Agile/Scrum development environments. 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. View the E-Verify posters here. 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 $145,600.00 to $209,300.00 USD. 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

• Design and ship AI agent systems end to end using Claude. • Build MCP connectors for platforms. • Use Claude Code in your daily engineering workflow. • Develop prompts and rubrics with eval sets. • Audit current automations for AI upgrade opportunities. • Propose and implement AI upgrades to existing workflows. • Write production-grade code in Python and TypeScript / JavaScript. • Set up observability for logging and monitoring. • Continuously audit MDS workflows for opportunities. • Partner with the Tech Lead and other specialists.

Description The Role We are looking for an ML tooling engineer to build tools to analyze and optimize distillation, training, and inference of ML models. You will develop and enhance GM's internal ML tooling for high performance software by leveraging state of the art tools like Nsight Systems, PyTorch, etc. The Autonomous Vehicle (AV) software stack heavily relies on machine learning models to perform critical driving tasks. These cutting-edge custom ML models require an ecosystem of in-house tooling to analyze and improve them. In this role, you will collaborate closely with engineers and researchers from different AV Engineering teams (e.g., Computer Vision, Perception, Behavioral Prediction) to scope out system requirements, while engaging with AV hardware teams to understand the target hardware platform and its constraints. What You'll Be Doing (Responsibilities) - Identify new opportunities to improve both training and inference efficiency - Build workflows for correctness and performance analysis on physical in-car compute and sensors - Build tooling to predict model performance based on architecture and data shape - Build tooling to trace actual performance on large distributed training and distillation jobs, running on the world's most powerful GPUs, and analyze the results - Continually evolve the toolchain and stack, to leverage the latest advancements in AI - Influence model architecture decisions and strategy within GM Your Skills & Abilities (Required Qualifications) - 3+ years of experience in the field of AI/ML - Experience with ML frameworks (e.g., PyTorch, TensorFlow) and NVIDIA developer ecosystem (TensorRT, Nsight-systems, Nsight-compute)) - Expertise in writing production quality Python/C++ code - Expertise in the software development life-cycle - coding, debugging, optimization, testing, integration - BS, or higher degree, in CS/CE/EE, or equivalent What Will Give You A Competitive Edge (Preferred Qualifications) - Experience developing and deploying machine learning models - GPU programming (CUDA) and familiarity with ML SW stack (e.g., cuDNN, cuBLAS) - Experience with ML accelerators and hardware architecture - 3+ years of experience in the field of AI/ML or relevant experience Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington. • The salary range for this role: is $144,700 to $261,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. • Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. • Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more. #GM-AV-1 This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. The selected candidate will be required to travel <25% for this role. This job may be eligible for relocation benefits. About GM Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all. Why Join Us We believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team. Total Rewards | Benefits Overview From day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources. Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire. Accommodations General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us [email protected] or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

• Design and build a unified evaluation platform that serves as the single source of truth for all of our agentic systems and audit workflows • Build observability systems that surface agent behavior, trace execution, and failure modes in production, and feedback loops that turn production failures into first-class evaluation cases • Own the evaluation infrastructure stack including integration with LangSmith and LangGraph. • Translate customer problems into concrete agent behaviors and workflows • Integrate and orchestrate LLMs, tools, retrieval systems, and logic into cohesive, reliable agent experiences • Build automated pipelines that evaluate new models against all critical workflows within hours of release • Design evaluation harnesses for our most complex Agentic systems and workflows • Implement comparison frameworks that measure effectiveness, consistency, latency, and cost across model versions • Design guardrails and monitoring systems that catch quality regressions before they reach customers • Use AI as core leverage in how you design, build, test, and iterate • Prototype quickly to resolve uncertainty, then harden systems for enterprise-grade reliability • Build evaluations, feedback mechanisms, and guardrails so agents improve over time • Work with SMEs and ML Engineers to create evaluation datasets by curating production traces. • Design prompts, retrieval pipelines, and agent orchestration systems that perform reliably at scale • Define and document evaluation standards, best practices, and processes for the engineering organization • Advocate for evaluation-driven development and make it easy for the team to write and run evals • Partner with product and ML engineers to integrate evaluation requirements into agent development from day one • Take full ownership of large product areas rather than executing on narrow tasks