Staff Forward Deployed Engineer

UKG•Seattle, WA

3d•$129,500 - $214,015•Hybrid

About The Position

UKG's Forward Deployed Engineering (FDE) practice is a small, elite team that embeds directly with our most strategic customers to solve high-value problems that sit just beyond the current product boundary. We deploy in small pods, move fast, learn the customer's world, and build things that work in production. The strongest FDEs combine technical depth with curiosity, creativity, perseverance, and customer empathy. They are comfortable with ambiguity because they are motivated by discovery: learning unfamiliar domains, finding the real problem, building the first working version, and turning what works into reusable capabilities for the broader platform. We are inspired by the best forward-deployed and outcome-delivery organizations in enterprise technology. Our operating model is simple: get close to the customer, define the outcome precisely, build something real, and use what we learn in the field to shape the future of the product. As a Staff Forward Deployed Engineer, you will lead technical discovery, solution design, and hands-on engineering for strategic customer engagements. You will sit at the intersection of customer needs, product possibility, and production software: turning ambiguous business problems into prototypes, architectures, integrations, and AI-enabled workflows that create measurable customer impact. At this level, you will typically own the technical direction for an engagement or major solution area, guide other FDEs, and shape reusable patterns for the broader practice. You are close to the customer and close to the code. You are expected to build, communicate, unblock, and raise the quality of the solution around you. Enterprise HCM, Payroll, or Workforce Management experience is helpful, but it is not the gatekeeper. We are looking for strong engineers who love learning unfamiliar domains, designing novel solutions, and pushing through ambiguity until something works in the real world.

Requirements

Typically 7+ years of experience building software, developer platforms, cloud applications, AI-enabled products, technical solutions, or production integrations.
Strong software engineering fundamentals across APIs, cloud services, data flows, testing, deployment, monitoring, debugging, and operational support.
Experience designing and deploying production systems or customer-facing technical solutions in ambiguous environments.
Demonstrated ability to lead technical direction for a workstream, engagement, or complex solution area.
Strong communication skills with the ability to explain tradeoffs clearly to technical teams, executives, and customer stakeholders.
Comfort learning unfamiliar domains quickly and turning incomplete information into practical technical plans.
Familiarity with modern cloud platforms such as AWS, Azure, or GCP.
Hands-on familiarity with AI-enabled tools, LLMs, copilots, workflow automation, or agentic patterns.
Sound judgment around security, privacy, compliance, reliability, and responsible use of AI.

Nice To Haves

Enterprise software experience is a differentiator, not a gatekeeper. Exceptional engineers from startup, SaaS, platform, developer tools, AI product, consulting, or modern enterprise software backgrounds are encouraged to apply.
Experience with HCM, Payroll, Workforce Management, ERP, IAM, SSO, or integration-heavy environments.
Experience with REST APIs, event-driven systems, ETL/ELT, data pipelines, workflow orchestration, or identity integrations.
Experience with AI agent patterns such as tools, orchestration, retrieval, evaluation, and human-in-the-loop controls.
Prior experience in a forward-deployed, solutions architecture, sales engineering, consulting engineering, or customer-facing product engineering role.
Experience turning field-built solutions into reusable product, platform, or reference architecture capabilities.

Responsibilities

Own Technical Discovery and Invention: Partner directly with customer stakeholders to understand workflows, pain points, data, systems, constraints, and opportunities for innovation. Ask sharp questions that reveal the real problem, not just the requested feature or implementation path. Translate ambiguous customer needs into hypotheses, prototypes, technical designs, implementation plans, and operational runbooks. Explore unfamiliar technologies, customer domains, and AI capabilities with curiosity, speed, and sound engineering judgment. Identify when a field-built solution should become a reusable platform capability or reference pattern.
Build and Ship Production Solutions: Design and build production-grade APIs, integrations, workflow automations, data pipelines, internal tools, and customer-facing technical solutions. Rapidly prototype ideas, test assumptions with customers, and mature the best ideas into maintainable production systems. Integrate UKG with HCM, Payroll, Workforce Management, ERP, IAM, data, and related systems using secure, auditable designs. Own technical delivery through discovery, design, build, testing, go-live, and early operational handoff. Make pragmatic tradeoffs across speed, quality, scalability, security, maintainability, and customer impact.
Lead the Engineering Pod: Set technical direction for an FDE engagement or major solution area and keep engineering work focused on the customer outcome. Guide other FDEs through architecture reviews, pairing, code reviews, technical planning, and high-judgment customer moments. Collaborate closely with the engagement PM, customer teams, Product, Engineering, Support, and Security to surface risks, dependencies, and decisions early. Communicate technical tradeoffs clearly to engineers, product leaders, executives, and customer stakeholders. Navigate difficult delivery moments with creativity, perseverance, and a bias toward making progress.
Shape Product and Practice: Contribute reusable components, templates, examples, reference architectures, demos, and implementation notes that accelerate future engagements. Provide clear feedback to Product and Engineering on platform gaps, repeatable customer needs, and opportunities to productize field-built solutions. Help develop the FDE playbook: better discovery methods, solution patterns, demo assets, tooling, and engineering practices. Identify expansion signals and help define the next high-value problems the team should solve for the customer.
AI-Enabled Workflow Responsibilities: Identify practical, high-impact uses of AI within existing enterprise workflows. Prototype and deploy AI-assisted patterns such as intelligent intake, summarization, recommendations, routing, data extraction, and human-in-the-loop review. Design for security, privacy, compliance, explainability, evaluation, monitoring, and operational control from the start. Apply sound judgment around model limitations, sensitive data, access controls, failure modes, and responsible use of AI. Turn successful AI patterns into reusable implementation guidance for the broader FDE library.