Applied AI Engineer, GTM Growth Engineering

Compensation

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this position is $230K – $385K, with generous equity, performance-related bonuses, and the following benefits:

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend to fuel your professional growth
• Daily meals in our offices, and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends

About the Team

GTM Growth Engineering builds AI-native products that help OpenAI's go-to-market and B2B marketing organizations scale with greater speed, intelligence, and operational effectiveness. Our work brings together software engineering, applied AI, product, data, and GTM operations.

About the Role

We're looking for an Applied AI Engineer to build production systems that help AI-powered go-to-market workflows improve over time. You will connect agent behavior, customer and operator feedback, evaluation, experimentation, and business outcomes to make these systems more effective, reliable, and responsive to evolving customer needs.

Responsibilities

• Own the production improvement loop across agent behavior, customer and operator feedback, evaluation, experimentation, and verified business outcomes.
• Instrument agent workflows so model interactions, tool use, decisions, failures, human edits, and downstream outcomes can be understood in context.
• Define meaningful quality standards, representative evaluation datasets, regression coverage, and production monitoring for real GTM workflows.
• Investigate why agents underperform across context, knowledge, instructions, tools, routing, guardrails, or workflow design.
• Design and ship targeted behavior improvements, including changes to prompting, context construction, decision logic, tool use, and human-review paths.
• Build backend services, APIs, data models, and feedback pipelines that make agent behavior observable, steerable, and reproducible.
• Run controlled experiments, production replays, or staged rollouts to measure whether changes improve quality and downstream business results.
• Partner with Product, Data Science, Sales, and B2B Marketing to prioritize high-value problems and define customer and business success.
• Ship with appropriate safeguards for privacy, security, reliability, human oversight, and safe operational rollout.

Requirements

• 4+ years of software, backend, applied AI, or product-engineering experience building reliable production systems.
• Experience building AI agents, LLM-powered applications, or other model-driven workflows that operated on real production traffic.
• Experience diagnosing and improving agent behavior using production traces, user feedback, evaluation, experimentation, or careful systems design.
• Practical experience with evaluation design, regression testing, human or model grading, online quality signals, or controlled experiments.
• Strong backend engineering skills across Python, APIs, data pipelines, stateful workflows, and production services.
• Strong product judgment and the ability to connect technical changes to customer experience, conversion, qualified pipeline, or operational efficiency.
• Comfort working across model behavior, context, knowledge, tools, workflow state, and human-in-the-loop decisions.
• The ability to work closely with technical and non-technical partners across Engineering, Product, Data Science, Sales, and B2B Marketing.
• A pragmatic mindset: you can scope ambiguous problems, ship useful improvements, and build toward a durable system.

Nice to Have

• Experience building agent evaluation, observability, experimentation, or AI infrastructure products.
• Experience with production replay, LLM grading, human-labeled datasets, shadow evaluation, or staged rollout.
• Experience improving model or agent behavior through context design, prompting, tools, decision logic, or feedback loops.
• Experience with sales, B2B marketing, revenue, CRM, campaign, or other GTM-facing systems.
• Experience measuring customer engagement, qualified pipeline, conversion, or operational efficiency.