Staff Software Engineer

About the role

You will design, build, and continuously deploy features across the full stack, owning the end-to-end delivery of capabilities that power the agency portal for senior living and home care providers. You will partner with product and design to craft partner-facing experiences in Grace that make referral management, community profiles, and reporting genuinely easier for business users. You will leverage AI coding agents as a core part of your daily workflow, scoping work for agents, reviewing and hardening their output, and establishing the context and prompts that make agentic development reliable. You will apply LLMs to product and data problems where they outperform hand-written rules, including extraction from unstructured provider records, classification, matching, and summarization, while implementing evaluation harnesses and guardrails. You will collaborate with leads to design cloud-native, service-oriented components and APIs behind a unified API layer, and enable continuous deployment by championing quality engineering practices such as linting, unit testing, integration and e2e testing, and pipeline automation. You will peer review code, suggest optimizations, and create reference implementations that raise the bar for the team while defining engineering best practices for how the team adopts and governs AI tooling. You will investigate and resolve production issues end to end, from the UI through the service and data layers, and work to prevent recurrence, and partner with product and project managers so deliverables land on time and at high quality. You will keep technology decisions tightly tied to business outcomes and communicate clearly with stakeholders to ensure alignment.

Key facts

What you'll do

• Design, build, and continuously deploy features across the stack - React/TypeScript front ends, .NET service and API layers, and the data pipelines behind them.
• Build partner-facing experiences in Grace that make referral management, community profiles, and reporting genuinely easier for agency users.
• Use AI coding agents as a core part of your daily workflow - scoping work for agents, reviewing and hardening their output, and building the context, prompts, and tooling that make agentic development reliable on our codebase.
• Apply LLMs to product and data problems where they outperform hand-written rules - extraction from unstructured provider records, classification, matching, and summarization - with evaluation harnesses and guardrails so quality is measured rather than assumed.
• Collaborate with leads to design cloud-native, service-oriented components and APIs.
• Enable continuous deployment by championing quality engineering practices: linting, unit testing, integration and e2e testing, and pipeline automation.
• Peer review code, suggest optimizations, and create reference implementations that raise the bar for the team.
• Help define engineering best practices including how the team adopts and governs AI tooling and provide technical mentorship.
• Investigate and resolve production issues end to end, from the UI through the service and data layers, and work to prevent recurrence.
• Partner with product and project managers so deliverables land on time and at high quality, and keep technical documentation for your areas current.

Requirements

• 10+ years of software engineering experience, with demonstrated depth on both sides of the stack.
• Strong proficiency with modern front-end development - React, TypeScript, component architecture, state management, testing, and design system adoption.
• Strong proficiency building and operating back-end services and APIs in any modern server-side language, plus solid relational data modeling and SQL.
• Working proficiency in Python for data ingestion, transformation, and automation.
• Demonstrated proficiency with AI-assisted and agentic development workflows - for example Claude Code or similar coding agents, MCP-based tool integrations, and AI-assisted code review - with concrete examples of how it changed your team's throughput or quality.
• Practical experience building LLM-backed features: prompt design, retrieval and context strategy, structured output, cost and latency management, and evaluating output quality systematically.
• Experience with cloud infrastructure (AWS preferred) and modern data platforms such as Databricks, Snowflake, or equivalent.
• Thrives in fast-paced environments while architecting dependable solutions that scale effectively.
• Role models and champions modern ways of working - Agile, DevOps, and related practices - and actively participates in an engineering community.
• Strong communication and storytelling skills, with experience translating technical concepts to non-technical audiences.

Practical notes

This is a remote, full-time role. You are expected to work during standard business hours and be available for meetings and collaboration as needed.