Staff Software Engineer - Developer Experience
Addepar1 · New York, NY; Remote, USA
About this role
**Who We Are** Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market, and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,500 firms in 60 countries use Addepar to manage and advise on nearly $10 trillion in assets. **The Role** We are seeking a highly proactive **Staff Software Engineer** to drive the architecture of next-generation validation guardrails and spearhead our AI-native developer experience across the organization. This role focuses on architecting engineering workflows across all traditional SDLC stages—Planning, Design, Build, Test, Deploy, and Maintain—into automated, closed-loop systems where AI agents and committed markdown artifacts accelerate delivery while keeping human engineers and product managers firmly in control of high-judgement decisions. As a technical thought leader, you will help lead the design of intelligent frameworks, AI SDLC playbooks, and deterministic guardrails required to modernize our SDLC. Operating with a high degree of autonomy, you will lead grassroots efforts across engineering teams, navigate technical ambiguity to advocate for best practices, mentor developers, and engineer a friction-free internal platform for the future. **What you’ll do** - **Technical Leadership & Grassroots Evangelism** - **Adoption & Mentorship:** Drive bottom-up adoption of AI-native development workflows by coaching engineers to transition into orchestration of parallel agentic sessions and subagents. - **Cross-Team Alignment:** Partner with platform engineers, R&D teams, and security to dismantle traditional handoffs and convert manual sign-off cycles into automated artifact triggers with clear governance boundaries. - **AI-Native Lifecycle Architecture** - **Artifact-driven workflows:** Operationalize markdown-driven artifact pipelines to create transparent audit trails from idea to production. - **Deterministic guardrails & hooks:** Architect deterministic build-time and deployment hooks to serve as safety boundaries behind AI development. - **Automated code review & PR pipelines:** Integrate AI into the PR review loop using structured policies and CI runners to enable agents to conduct multi-pass reviews and autonomously resolve comment threads. - **Continuous Evals, Testing & Closed-Loop Operations** - **Feedback loops & verifications:** Design deterministic verification harnesses and visual feedback loops so AI agents can self-test, iterate on defect discoveries, and verify correctness before human review. - **Continuous eval infrastructure:** Build continuous evaluation suites in CI to regression-test AI steering configs against sets of real engineering tasks whenever prompts or models change. - **Autonomous closed-loop maintenance:** Implement automated production monitoring scripts to detect control breaches (failure spikes, latency drift) and design triage queues that feed back into improvements to close the SDLC loop. **Who You Are** - **Architectural mastery:** Deep conceptual and hands-on understanding of integrating AI into the development lifecycle across the entire SDLC. - **Data-driven core:** Reject gut feelings as the driver; demonstrate success improving DORA metrics and framework reliability using explicit data analysis. - **High autonomy & proactivity:** Thrives in ambiguity—
Listing freshness
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