---
title: 'AI Engineer at Topsort'
canonical: 'https://feeny.ai/job/ai-engineer-topsort-santiago-santiago-metropolitan-region-n9g82prk2sa0'
type: 'job'
last_seen: '2026-11-04'
---

# AI Engineer at Topsort

- **Company:** Topsort
- **Location:** Santiago Santiago Metropolitan Region, Chile
- **Posted:** 2026-10-22
- **Last confirmed live:** 2026-11-04
- **Apply:** https://job-boards.greenhouse.io/topsort/jobs/5461078008

## Job description

Come build what's next with Topsort!

## About Topsort

Topsort is a global Commerce AI company making advanced advertising and AI-powered technology accessible to businesses of all sizes.

Our technology helps retailers and marketplaces grow through smarter advertising, real-time auctions, and intelligent product discovery. With a presence in 40+ countries and a growing international team, we're building the technology behind the next generation of commerce.

What Is This Role Like?

We're looking for an AI Engineer to join our T-Brain team and help build the intelligence powering the next generation of commerce.

T-Brain is Topsort's AI platform, developing machine learning models that power predictions, recommendations, and smarter decision-making across retail environments.

In this role, you'll work at the intersection of Machine Learning, Data Science, and Software Engineering, developing models, running experiments, and turning complex data into scalable, production-ready solutions.

## What You'll Own

- Machine Learning Development: Design, train, evaluate, and improve ML models for predictions, recommendations, and other commerce applications.
- Experimentation & Validation: Design and run experiments, including A/B testing, to evaluate model performance and make statistically grounded decisions.
- Production ML: Build, deploy, and maintain reliable ML solutions using cloud infrastructure, ensuring scalability and performance.
- Data & Modeling: Work with complex datasets to identify patterns, develop predictive models, and improve model effectiveness.
- Model Innovation: Contribute to the development of large-scale foundation models and explore how experimental findings can be applied across different use cases.

What Success Looks Like

- Production-Ready Models: ML models are reliably deployed, maintained, and performing at scale in production environments.
- Validated Improvements: Experiments demonstrate measurable improvements against established baselines, supported by sound statistical evidence.
- Model Performance: Predictions and recommendations deliver consistent, measurable improvements in accuracy, relevance, or other defined performance metrics.
- Scalable Solutions: Models and experimental findings can be adapted and extended across different clients and retail use cases.
- Continuous Improvement: Monitoring, experiment results, and changing data patterns inform ongoing improvements in model performance and reliability.

## What You'll Bring

Don't check every box? We'd still love to hear from you. We value curiosity, strong judgment, and people who take ownership of results:

- Relevant Experience: 5+ years of experience in Machine Learning Engineering, Applied Machine Learning, or Data Science, with hands-on experience deploying models in production.
- Academic Background: BSc. degree in Computer Science, Software Engineering, Mathematics, Statistics, or a related quantitative field.
- Machine Learning: Strong experience developing, training, evaluating, and optimizing ML models for real-world applications.
- Cloud ML: Hands-on experience with cloud platforms such as AWS, GCP, or Azure, including model training, deployment, and scalable ML pipelines.
- Python & Data: Strong Python skills and experience with SQL, data processing, and working with large datasets.
- Experimentation: Solid statistical foundations and practical experience designing, running, and interpreting A/B tests to evaluate model performance.
- Software Engineering: Strong coding practices and experience building reliable, maintainable, production-ready ML systems.

Nice To Have:

- Experience in e-commerce, Retail Media, AdTech, or personalization.
- Experience with recommendation systems, search relevance, retrieval, or ranking models.
- Familiarity with deep learning architectures, including neural networks and transformers, using frameworks such as PyTorch or TensorFlow.
- Familiarity with emerging ML research and experience applying new techniques to practical problems.

Who Thrives Here

We set a high bar for engineering because the systems we build matter. You'll thrive here if you:

- Ownership & Quality: You take responsibility for what you build, write production-ready code, and leave systems better than you found them.
- Engineering Judgment: You make thoughtful technical decisions, communicate tradeoffs honestly, and balance speed with long-term reliability.
- Collaboration & Communication: You share knowledge, communicate openly, and work effectively across teams to solve complex challenges.
- Curiosity & Attention to Detail: You care about the details, continuously improve your skills, and explore new technologies with a critical mindset.

We are proud to be an equal opportunity employer and to foster a workplace where diversity is valued and celebrated. We believe that diverse perspectives and experiences strengthen our teams and drive better outcomes. We respect and value any race, religion, color, national origin, ancestry, caste, gender, gender identity or expression, sexual orientation, age, disability, medical condition, pregnancy, genetic information, marital status, or military service.

Do you sound like the right fit? Let's dive right in!

## About Topsort

## Company Overview
- **One-liner**: Topsort provides AI-powered, privacy-first retail media infrastructure that enables marketplaces, retailers, and commerce platforms to launch and scale monetization through real-time auctions, sponsored listings, display, offsite, and in-store advertising.
- **Entity Type**: Private (Series A, post-money valuation $150M as of March 2024)
- **Headquarters**: Palo Alto, California, United States
- **Founded**: 2021
- **Founders**: Not publicly available (company spokesperson identified as "Ye" in media)

## Core Business
- **Primary industry/industries**: Retail media infrastructure, ad tech, commerce monetization, AI-powered advertising
- **Target customers**: B2B – marketplaces, retailers, delivery apps, travel platforms, and commerce networks (both enterprise and SMB)
- **Mission or purpose statement**: "Make world-class monetization accessible to everyone" by bringing the same auction and AI-driven advertising infrastructure used by the largest marketplaces to every commerce platform.

## Products & Services
- **T-Engine (Auction & Ranking Engine)**: High-performance, edge-deployed auction infrastructure for large-scale commerce media – sub-5ms p99 latency, 99.99% uptime SLA.
- **Toptimize (AI Optimization Layer)**: Improves yield, pacing, ROAS, and relevance on top of existing ad stacks.
- **T-Brain (Commerce Intelligence)**: Uses catalog, auction, campaign, and performance data to power smarter monetization and ranking.
- **Data Genie (Retail Media Analytics)**: Unifies reporting, insights, benchmarking, and performance analysis.
- **Tomi (AI Ad Ops Agent)**: Conversational AI agent for launching and managing retail media campaigns.
- **Sponsored Listings**: Auction-based product promotion in search, category, and discovery surfaces.
- **Sponsored Brands**: Brand-level advertising across commerce surfaces.
- **Display & Banners**: Programmatic display advertising.
- **In-Store Media**: Extends retail media into physical store environments.
- **Sponsored Prompts**: Agentic ad formats for AI-native commerce (e.g., within conversational shopping experiences).
- **BIDLESS™**: Autobidding technology for automated campaign optimization.

## Market Standing
- **Valuation/Market Cap**: $150 million post-money (Series A, March 2024); previously $110 million post-money (seed round, 2022)
- **Key Metric**: Total funding raised – $28.6 million ($8M seed in 2022, $20M Series A in 2024)
- **Notable Investors/Partners**: Upload Ventures (lead Series A), Quiet Capital, Pear Ventures
- **Growth Signals**: 120+ employees across 4 continents; 40+ countries served; billions of commerce decisions processed; <5ms auction latency; 99.99% uptime; expanded into offsite and in-store media in 2023–2025; launched new ad formats (Sponsored Brands, Video Ads, Sponsored Prompts) and AI agents (Tomi, Data Genie).

## Competitive Advantages
- **Privacy-first approach**: Built for a post-cookie world with a Cleanroom feature that combines data without compromising user privacy.
- **AI-native infrastructure**: Auctions, relevance, measurement, and optimization are designed for AI-driven commerce (including agentic shopping).
- **Omnichannel coverage**: Supports search, category, display, offsite, in-store, and emerging AI prompt surfaces from a single API.
- **Low latency at global scale**: Edge-deployed auction engine with sub-5ms p99 latency across nine regions.
- **Accessibility**: Democratizes advanced monetization technology previously limited to giants like Amazon, making it available to smaller marketplaces and retailers.

## Strategic Focus
- **AI-native commerce**: Expanding monetization formats for agentic and conversational shopping experiences (e.g., Sponsored Prompts).
- **Global expansion**: Growing presence across North America, Europe, Asia Pacific, and Latin America.
- **Product breadth**: Continuing to add ad formats (video, in-store, offsite) and AI-powered automation (Tomi, Data Genie, Toptimize).
- **Post-cookie leadership**: Positioning as the clean, privacy-compliant alternative for retail media.

## Why Work Here
- **Culture**: Described as "smart, driven, and from all over the globe" – a team building infrastructure that powers the next generation of commerce.
- **Engineering focus**: Engineers work on high-scale auction systems, AI/ML optimization, distributed systems, and real-time data pipelines.
- **Global team**: Offices in Palo Alto (HQ), Munich, Melbourne, Santiago, and Barcelona; remote-friendly culture implied by distributed team.
- **Growth trajectory**: Fast-growing startup (Series A, 120+ employees) with a clear product-market fit in a hot sector (retail media).
- **Impact**: Opportunity to build the underlying monetization infrastructure for hundreds of commerce platforms, processing billions of decisions.

## Sources
1. [Topsort Website](https://www.topsort.com/)
2. [Topsort About Page](https://www.topsort.com/about)
3. [Topsort Careers (Greenhouse)](https://job-boards.greenhouse.io/topsort)
4. [Topsort LinkedIn](https://www.linkedin.com/company/topsort)
5. [TechCrunch: "Topsort helps e-commerce create ads without being ‘creepy’"](https://techcrunch.com/2024/03/04/topsort-150m-e-commerce-ads/)

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