Software engineer - Applied Growth, Monetization & AI Engineer Vertical Search, Intelligence and Digital Publishing

INSEAD; Austin (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

INSEAD is seeking a hands-on software engineer to propel growth, monetization, and AI-enabled workflows across large digital properties. You will write production Python and C# code, leverage agentic systems, and manage data pipelines while building tools and experiments that drive measurable revenue improvements.

The role emphasizes ownership of end-to-end systems, collaboration with data teams, and rigorous testing, with a focus on scalable, fast, and secure software that supports ad tech and

Qualifications

• Hands-on production work in applied engineering roles over the last 3 years.
• Python is mandatory; production-quality Python for analysis, APIs, automation, data pipelines, experimentation, and tools.
• Experience using agentic coding systems (Claude Code, Codex) to build software and validate outputs.
• Strong SQL and experience with Git, PRs, testing, APIs, cloud systems, and data warehouses (BigQuery, Snowflake, Redshift, Postgres).
• Demonstrated ownership of systems you built or improved with measurable results.

Responsibilities

• Build scalable SEO, structured-data, crawl, indexation, and local-search systems across thousands of properties.
• Develop monetization across direct sales, sponsorships, newsletters, programmatic advertising, and data products.
• Work with ad tech systems: Google Ad Manager, Prebid, SSPs, pricing rules, inventory taxonomy, forecasting, reporting, and QA.
• Design and run experiments with randomization, holdouts, power analysis, and causal measurement.
• Create data layers, dashboards, quality controls, pricing tools, and experiment-management infrastructure.
• Develop agentic workflows for research, classification, QA, and sales preparation with human reviews.

Skills

Python

Claude/Codex

SQL

Git

Cloud platforms

BigQuery

Snowflake

Postgres

Tools

Claude Code

Codex

APIs

Analytics tools

Job description

Software engineering - Applied Growth, Monetization & AI Engineer

Vertical Search, Intelligence and Digital Publishing

We are building a large portfolio of focused digital properties for professional, geographic, institutional, and enthusiast audiences.

This is a hands‑on individual‑contributor engineering role. It is not a senior‑manager, director, VP, consultant, or strategy‑only position. You will personally write code (Python, C#), agentic systems (Claude, Codex) and SQL, build tools and automations, instrument products, run analyses and experiments, and improve live systems.

Required profile

• You are currently a practicing applied engineer, growth engineer, data/product engineer, monetization engineer, ad‑tech engineer, or comparable technical individual contributor.
• You have spent at least the last three years doing hands‑on production work in relevant areas—not merely managing, advising, or delegating it.
• Python is mandatory. You must be able to build, test, maintain, and debug production‑quality Python for analysis, APIs, automation, data pipelines, experimentation, and internal tools.
• You must have recent, practical experience using Claude Code, Codex, or comparable agentic coding systems to build software. You know how to give agents useful tasks, inspect their output, test and debug it, maintain security and code quality, and take responsibility for what reaches production.
• You have strong SQL and experience with Git, pull requests, testing, APIs, cloud systems, and a warehouse or analytical database such as BigQuery, Snowflake, ClickHouse, Redshift, or Postgres.
• You can show systems you personally built or materially improved, the constraints involved, and the measurable result.

What you will do

• Build scalable SEO, structured‑data, internal‑linking, crawl, indexation, canonicalization, local‑search, and performance systems across thousands of properties.
• Build portfolio triage and launch systems that identify which properties to scale, improve, reposition, consolidate, pause, or retire.
• Build and improve monetization across direct sales, sponsorships, newsletters, programmatic advertising, lead generation, directories, data products, research, events, and premium intelligence services.
• Work directly with Google Ad Manager, header bidding, Prebid, SSPs, pricing rules, inventory taxonomy, forecasting, reporting, demand quality, and ad‑operations QA.
• Improve net revenue per session and contribution margin without sacrificing user experience, page speed, search health, advertiser quality, privacy, editorial independence, or brand safety.
• Build the event, revenue, cost, inventory, advertiser, and user‑behavior data layer; dashboards; quality controls; pricing and sales tools; and experiment‑management tools.
• Design and run sound experiments with randomization, holdouts, power analysis, causal measurement, decision thresholds, and rollback criteria.
• Apply practical ML or optimization only when it creates real economic value: recommendations, registration prompts, ad layout, floor prices, demand paths, content promotion, sponsorship offers, and sales prioritization. Use shadow mode and staged deployment before broad automation.
• Build reliable agentic workflows for research, classification, reporting, QA, metadata generation, structured‑data validation, and sales preparation, with appropriate evaluation and human review.

Technical strengths we need

• Advanced SQL: analytical and optimized queries, cohorts, funnels, attribution, and data‑quality checks.
• Python, pandas/NumPy, and practical familiarity with statistical or ML tools such as scikit‑learn, statsmodels, XGBoost, LightGBM, or PyTorch.
• Sound practical statistics: experiments, regression, forecasting, causal inference, selection bias, and confounding. Experience with bandits, ranking, recommendations, or off‑policy evaluation is valuable.
• Publisher/ad‑tech knowledge: Google Ad Manager, header bidding, Prebid, programmatic demand, floor strategies, viewability, invalid traffic, ads.txt, sellers.json, consent, tag management, Analytics, Search Console, and first‑party data.
• Good engineering judgment: recognize when a simple rule or A/B test beats custom ML; detect data leakage, drift, poor calibration, and unintended product or revenue effects.

What success looks like

Within a year, you will have built a trusted measurement and revenue model; a clear portfolio‑opportunity system; scalable SEO, quality‑control, experimentation, and monetization operations; measurable growth on selected properties; and improved qualified traffic, net revenue per thousand sessions, advertiser retention, and contribution margin.

What we will ask you to demonstrate

• A recent Python or SQL system you personally built and shipped.
• Your real agentic development workflow using Claude Code, Codex, or a comparable system, including how you validate outputs.
• A traffic, revenue, RPM, fill, viewability, retention, or margin gain you helped deliver.
• An experiment or optimization you designed: hypothesis, measurement, result, and decision.
• A failed technical, growth, or monetization initiative and how you diagnosed it.
• How you would prioritize 1,000 low‑traffic properties with limited capital over six months.

How we work

We value measurable outcomes, technical rigor, high‑quality products, controlled experimentation, and durable economics. We do not value vanity traffic, indiscriminate AI‑generated content, unexplained black‑box tactics, or short‑term revenue that weakens the product.