Addepar Staff Software Engineer at Addepar to architect next-generation validation guardrails and AI-native developer experience. Lead design of intelligent frameworks and AI SDLC playbooks to modernize software development lifecycle.
Responsibilities
We are seeking a highly proactive Staff Software Engineer to drive the architecture of our 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 system where AI agents and committed markdown artifacts are able to 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 ambiguitity to advocate for best practices, mentor devleopers and engineer the friction-free internal platform for the future.
Applicants must be legally authorized to work in the United States for any employer without requiring current or future visa sponsorship (for example, employment-based visas such as H-1B, F-1/OPT, or similar), and must be authorized to begin work in the U.S. on their first day of employment.
Technical Leadership & Grassroots Evangelism
Adoption & Mentorship: Drive bottom-up adoption of AI-native development workflows across product teams 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 piplines 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 PR review loop using structured policies and CI runners to enable agents to conduct multi-pass reviews and autonomously resolve comment threads.
Feedback loops & verifications: Design deterministic verification harnesses and visual feedback loops that allow AI agents to self-test, iterate on defect discoveries, and verify correctness before going for human review.
Architectural Mastery: Deep conceptual and hands-on understanding of integrating AI into the development lifecycle across the entire SDLC.
Data-Driven Core: A mindset that completely rejects gut feelings as the driving factor. You have demonstrated success moving the needle on DORA metrics and framework reliability using explicit data analysis.
High Autonomy & Proactivity: Thrives in highly ambiguous environments. You don't wait for a ticket to be assigned; you actively detect friction, build the business case, and engineer the solution.
Matured Engineering Skills: Strong programming skills in languages such as Python, Java, or JavaScript, with an emphasis on writing clean, maintainable, production-grade tools.
Modern CI/CD Expertise: Deep hands-on experience with advanced CI/CD orchestration (e.g., GitHub Actions, Jenkins), infrastructure-as-code (Terraform), and containerization (Docker, Kubernetes).
[Preferred] Experience managing complex software states and mimicking/isolating environments utilizing tools at enterprise scale.
[Preferred] Past experience building custom internal wrapper applications or developer plugins around Large Language Models (LLMs).
[Preferred] Proven success technically leading or closely pairing across split North American and Indian engineering hubs.
[Preferred]Familiarity with service integration testing, contract testing tools, and developer catalog platforms (e.g., Backstage).
Addepar takes a market-based approach to pay. A successful candidate's starting pay will be determined based on the role, job-related skills, experience, qualifications, work location, and market conditions. The range displayed on each job posting reflects the minimum and maximum target base salary for this role within the United States.
