Senior Software Engineer, Applied AI

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Software Engineer, Applied AI based in the United States.

We are seeking an experienced software engineer to transform how a fast-growing, profitable technology business operates through advanced AI and large language models (LLMs). In this role, you will design, build, and deploy intelligent agents that automate workflows across functions including sales, revenue operations, editorial, and people operations. You will own the entire development lifecycle, from identifying opportunities and defining specifications to building systems, creating evaluations, and rolling out solutions to internal teams. Rather than simply automating existing tasks, you will help establish a model in which workflows continuously improve as AI capabilities advance. Working closely with product leadership and business stakeholders, you will have substantial autonomy to shape technical and product decisions. This is an opportunity to make a company-wide impact in a lean, remote-first environment with minimal legacy constraints and a strong focus on experimentation, measurable outcomes, and rapid execution.

Accountabilities

• Build AI agents and platform capabilities: Design, develop, and deploy new agents, reusable skills, and foundational AI capabilities that improve workflows across multiple business functions.
• Own end-to-end delivery: Lead the complete implementation lifecycle, from identifying use cases and designing solutions to coding, evaluation, deployment, and adoption by internal users.
• Define quality standards: Write clear technical specifications, evaluation criteria, and test cases that establish measurable definitions of success for AI-powered workflows.
• Develop and maintain evaluations: Integrate evaluations into the development process, measure agent performance, identify weaknesses, and refine systems based on evidence and observed results.
• Identify automation opportunities: Collaborate directly with stakeholders in sales, revenue operations, editorial, and people operations to understand their processes and determine where LLMs can deliver meaningful improvements.
• Drive product and technical decisions: Partner with the Applied AI Product Manager to prioritize initiatives, while independently making smaller product decisions and determining the best implementation approach.
• Improve workflows continuously: Build systems that take advantage of improving model capabilities, enabling processes to become more effective over time without requiring repeated manual redesign.
• Promote AI-first operations: Help shift teams away from manual task execution toward defining specifications, establishing quality benchmarks, and supervising AI-driven workflows.
• Deliver measurable business impact: Focus on practical improvements in productivity, quality, and operational efficiency, ensuring AI implementations produce observable results rather than relying on anecdotal feedback.
• Operate with autonomy: Take ownership of projects from conception through production, identify what should and should not be built, and deliver solutions without waiting for every detail to be specified.

Requirements

• Software engineering experience: At least five years of professional experience in software engineering or a closely related technical role.
• Applied AI expertise: At least two years of experience building products or systems powered by large language models (LLMs).
• Production agent ownership: Demonstrated experience taking at least one AI agent into production and continuing to own and improve it.
• AI-assisted development: Strong coding proficiency with AI development tools such as Claude Code or Codex, using them effectively to accelerate implementation and improve engineering workflows.
• Evaluation-driven development: Experience designing and incorporating evaluations throughout the development lifecycle, with the ability to explain success criteria, measurement methods, and improvements made based on results.
• End-to-end execution: A track record of taking projects from initial concept through implementation and deployment, with the initiative to resolve ambiguity and move work forward independently.
• Product judgment: Ability to assess business needs, make sound technical trade-offs, prioritize effectively, and explain why certain features or solutions should not be purs…