Senior Software Engineer, AI-Augmented Development

Senior Software EngineerAt ACT, your work makes a difference. Education has power – a power that changes lives forever. It creates opportunities that lift up individuals, their families, and sparks societal change that echoes through generations to come. From our grassroots we have fought the good fight for inclusivity in education, and we remain devoted to helping anyone who struggles to access that power. This is what matters to us and we must do better -- we've never been more sure of our purpose.ACT team members are part of an organization dedicated to a mission that has never been more important: Helping people achieve education and workplace success. Advancing that mission within our organization, by helping our team members achieve education and workplace success of their own, is core to our values. ACT values a diverse workplace and is committed to fostering an inclusive environment in which every team member has an equal opportunity to grow.We want our team members to have the well-being and confidence they need to do their best work, in an environment where both they and ACT thrive. To support this, our total rewards include company paid life insurance, medical, dental, vision, flexible health and dependent spending accounts, 401(k) retirement savings with company match, paid holidays, paid time off, and so much more. You can find a comprehensive list of our benefits here.We are seeking a Senior Software Engineer, to help us fulfill that mission.The Senior Software Engineer applies advanced knowledge, skill, and expertise to software delivery. Work is both independent and collaborative in nature. This role emphasizes using AI coding tools to develop software efficiently, while owning the judgment calls AI can't make, including reviewing, testing, and correcting AI-generated work, and taking end-to-end ownership of delivery from specification through production. Beyond using AI tools well, this role is expected to actively drive how the team adopts new AI-assisted development practices by piloting new tools and techniques, and championing what actually works. This is primarily a hands-on senior engineering role within the team, with the opportunity to grow into a cross-team responsibility for advancing how the broader engineering organization uses AI-assisted development tools as the role matures.The finalist will be hired at the salary and level commensurate with their qualifications. For this position, we anticipate offering an annual salary between $110,000 and $135,000. ACT manages salaries within this range based on relevant factors, including skills, experience, and internal equity. This position is not incentive eligible.Location: This is a remote position, based in the United States.Visa: Applicants must be authorized to work in the U.S. without the need for visa sponsorship.What you will be working on:Technical Leadership & Complex Problem-Solving Solve complex technical problems across the team's core systems, using AI-assisted approaches to move faster, including integrations with related platforms.Contribute senior-level technical judgment to active modernization initiatives within the team, using AI tools to accelerate legacy-to-modern platform migrations.AI Tooling Strategy & Adoption Pilot new AI-assisted development tools, models, and techniques within the team (e.g., new agentic coding workflows, MCP integrations, updated model versions), evaluating them against real delivery work rather than vendor claims.Propose and champion new use cases for AI in the team's engineering practice, backed by a measured before/after comparison — time saved, defect rate, or review burden.Feed adoption learnings back into team practice, updating norms, playbooks, or tooling recommendations as new approaches prove out (or don't).Deep Domain Knowledge Apply deep familiarity with the team's platform architecture, history, and constraints to guide technical decisions, including where AI-assisted approaches can speed modernization work.AI-Directed Development & Delivery Ownership Direct AI coding tools and agents to implement features from specs, tickets, or requirements, and own end-to-end delivery of the feature from specification through production.Review, test, and correct AI-generated code for correctness, security, and architectural fit; catch both subtle errors (a plausible-but-wrong calculation, an approach that won't scale) and obvious ones.Act as a steward against AI-specific security and supply-chain risk, scrutinizing AI-suggested dependencies, flagging unfamiliar or unverified packages before they're accepted, and treating AI-generated code that touches auth, data access, or external integrations with elevated review.Write specs detailed enough for an AI tool to implement correctly on the first or second pass; for ambiguous problems, exercise judgment on whether to escalate or proceed independently.Maintain and extend the team's eval and guardrail practices that catch AI-introduced regressions before they reach production.Analysis, Recommendations & Autonomy Independently evaluate where AI-assisted development tools, primarily Claude Code, alongside comparable tools as needed, add genuine value, for example, scaffolding and legacy code comprehension, while applying extra human scrutiny to auth-adjacent or compliance-sensitive logic.Recommend and refine engineering practices with a high degree of autonomy, guided by high-level direction and using independent judgment to accomplish goals.Acts as a Resource for Colleagues Proactively share and teach AI-assisted development practices with the team (code review norms, prompt approaches, and verification habits for AI-generated output) with the opportunity to extend this to other engineering teams as the role grows.Operate as an individual-contributor role, building credibility and driving adoption of good practice through technical expertise and collaboration.Program & Initiative Leadership (Growth Path) As the role matures, lead or contribute to a cross-team initiative establishing shared, practical standards for responsible AI tool usage in engineering, including guidance on what's safe to share with external AI tools, and when AI-generated code warrants elevated review.Participate in the engineering support rotation per the team's standard model.Role Progression:The following outlines key milestones and expectations for the first year in this role.3-Month MarkCompleted onboarding on the team's platform architecture and AI tooling conventions; has shipped a few small features or fixes through AI-directed development, with work reviewed and merged at the team's normal quality bar.Is closely working with other team members to understand production issues and system configurations, and has demonstrated at least one concrete instance of catching an AI-generated error or gap before it shipped.6-Month MarkOwns a feature or workstream end-to-end — writing the spec, directing AI-assisted implementation, and handling review and testing — with minimal day-to-day oversight from the lead.Has contributed a concrete improvement to the team's AI-assisted development practice (a new guardrail, a refined spec template, a prompt pattern) that at least one other engineer has adopted.Demonstrates a working understanding of the end-to-end architecture of the platforms they support, including how those platforms integrate with other systems across the broader technology ecosystem, and the underlying infrastructure architecture beneath them.12-Month MarkHas led a modernization effort or comparable initiative to completion, with AI-directed development visibly accelerating delivery versus a traditional approach.Is beginning to extend influence beyond the team, if the opportunity has arisen — through early contributions to the cross-team AI standards initiative, informal mentoring, or a guardrail others have started using.This could be the job for you if you have (minimum requirements):Minimum of 5 years of progressively responsible software engineering experience.Engineering Fundamentals Object-oriented and/or functional programming fundamentals.Data modeling and query performance reasoning, across relational and/or schema-driven data layers.API design fundamentals: contracts, versioning, and backward compatibility, whether REST or schema-first (e.g., GraphQL).Security fundamentals: authentication/authorization flows, common injection classes, and dependency