Senior Forward Deployed Engineer (Remote Build)
New
R
RemoteHR Technology
Location: US, Ability to overlap with customer working hours as neededFull-TimeSenior
Salary$53,300 to $215,750
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Job Details
- Languages
- English
- Required Skills
- Backend DevelopmentFull Stack DevelopmentOAuthSaaS
Requirements
- Demonstrated experience shipping production software (backend and/or full-stack) and owning it in production (on-call mindset, debugging, instrumentation).
- Strong integration and systems thinking: APIs, event-driven systems, webhooks, auth (OAuth/service accounts), data modeling, idempotency, and failure modes.
- Applied AI/LLM experience beyond prototypes: prompt+tool design, RAG and retrieval patterns, evaluation, latency/cost trade-offs, and reliability practices.
- Ability to operate in ambiguity: you can go from problem statement to shipped solution with minimal hand-holding.
- Clear, structured communication in writing; ability to explain trade-offs to technical and non-technical stakeholders.
- Experience with enterprise environments (security reviews, compliance constraints, procurement, change management).
- Experience building with agent frameworks and/or workflow orchestration systems.
- Familiarity with HRIS, payroll, identity/access management, or adjacent enterprise domains.
- Experience with multi-tenant SaaS, RBAC, and data privacy by design.
Responsibilities
- Partner with customers to understand operational challenges, constraints, and success criteria; run structured technical discovery and define the definition of done.
- Translate open-ended problems into clear technical designs and implementation plans (architecture, data flows, integration surfaces, security considerations, rollout strategy).
- Build AI-enabled solutions that integrate with customer systems and Remote’s platform (e.g., data pipelines, workflow automations, tool integrations, agentic services) and perform real tasks end-to-end.
- Own deployments including reliability, performance, observability, incident response, and continuous improvement.
- Develop evaluation and measurement for AI behavior: success metrics, test harnesses, golden datasets, regression suites, and guardrails.
- Work with real data to validate outputs, quantify impact, and iterate quickly based on evidence.
- Collaborate cross-functionally with Product and Engineering to ship high-quality solutions and to upstream reusable components.
- Create leverage by turning one-off solutions into repeatable playbooks, reference architectures, templates, and internal tooling.
- Be the voice of the field: bring back concrete customer needs, edge cases, and constraints that should shape product strategy.
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