SENIOR AI ENGINEER, PERSONALIZATION

Posted Yesterday
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Stockholm, SWE
In-Office
Senior level
Artificial Intelligence • eCommerce • Machine Learning • Software
The Role
Design and build production-grade personalization and recommendation systems using sequential modeling and online learning. Implement feature engineering pipelines for high-volume event streams, real-time content delivery, exploration-exploitation frameworks, feedback/learning infrastructure, and evaluation methods to ensure genuine personalization while avoiding overfitting.
Summary Generated by Built In

Who We Are

We are a well-funded, early-stage technology company on a mission to fundamentally redefine the way the world searches, discovers, and transacts, built on a philosophy that puts the user first at every step.

 

The user is not simply a means of transaction. They are a valued participant whose data works for them, not against them, enriching their world and their own experience within it, with them retaining full control.

 

Our mission is underpinned by proprietary, patent-pending technology built on a novel engine and system.

The Problem You Will Be Solving

The internet was built to inform the many, not to serve the individual. Platforms optimize for engagement, extraction, and consumption at global scale. AI has made this model even more efficient, with the user still largely remaining the input, not the full beneficiary.

 

The problem is not information. The world has never had more. The problem is that people give away a lifetime of data without receiving the personalized experience it should enable.

 

People should not have to search. They should simply find.

Who You Are

Highly talented and motivated to build products with people, for people

You thrive in zero-to-one environments and find genuine satisfaction in creating from scratch

A fast-paced thinker with a relentless desire to learn, improve, and raise the standard around you

Able to inspire, elevate, and collaborate; you make the people around you better

A great communicator; you express complex ideas with clarity, precision, and confidence across all levels of the organization

Able to transform ideas into working systems and technical milestones with light speed and precision

Highly ambitious and action-driven; you thrive under pressure and find genuine energy in working at the edge of what is considered possible

Capable of designing and building production-grade systems in a fourth of the time most experienced and “sane” professionals would consider possible

 

What You Will Do

Build personalization systems that combine behavioral modeling, dynamic user representations, and real-time context-aware content delivery

Design and operate feature engineering pipelines that transform high-volume behavioral event streams into model-ready inputs within the latency and reliability constraints

Build recommendation systems that scale seamlessly from cold-start to data-rich production environments without requiring architectural changes

Implement exploration-exploitation frameworks such as contextual bandits, online learning, or reinforcement learning approaches that continuously optimize content delivery without heavy offline retraining cycles

Build feedback and learning infrastructure that translates user interactions into measurable model improvements on a continuous basis

Design evaluation frameworks capable of distinguishing genuine personalization quality from overfitting to easily measured proxy metrics

Minimum Qualifications

MSc in Machine Learning, Computer Science, Statistics, or a directly related quantitative field from KTH, Chalmers, ETH Zurich, EPFL, or an institution of equivalent standing

Deep theoretical and applied understanding of recommendation systems; has implemented methods from the literature and shipped them to real users

Experience with sequential modeling applied to user data; recurrent architectures, attention-based sequence models, or equivalent

Production ML engineering discipline; code that serves reliably in production, not only evaluates well in notebooks

Demonstrated ability to take a recommendation system from research to production

Preferred Qualifications

PhD in Machine Learning, Computer Science, or a directly related quantitative discipline; given significant weight in our assessment

Research depth in recommendation, sequential modeling, or online learning

Experience with privacy-preserving approaches to behavioral modeling

2–5 years of experience; exceptional graduates assessed on equal terms with experienced engineers

A body of independent work; published research, open source contributions, or personal projects that demonstrate what you build without instruction

What We Offer

Expect the most talented, rigorous, and driven colleagues you have worked with; people who lift each other up and hold each other to the highest standard. This company is driven by ideas and innovation, regardless of age, position, or background. The best ideas must win and the people behind them recognized and rewarded.

 

A home for exceptional people and geniuses within their domain; those who have always known what they are capable of and have been waiting for the environment to prove it to the world, whilst first being seen and supported by their coworkers.

 

Compensation includes an attractive salary and meaningful equity ownership for an early-stage startup.

Equal Opportunity

We are an equal opportunity employer committed to building an inclusive, high-performance culture where everyone can do their best work and reach their full potential. We welcome applicants of all backgrounds and do not discriminate on the basis of race, color, religion, national origin, gender, gender identity or expression, sexual orientation, age, disability, or any other characteristic protected by applicable law.

Application

To apply, send your CV/resume, GitHub profile, LinkedIn profile, and a brief note on how and why you would like to contribute to our mission. Upon initial acceptance, an in-person interview will follow shortly in Stockholm, Sweden.

 

Please note that all applications and interviews are conducted in English. Fluency in written and spoken English is a requirement for all roles.

 

All applications are treated with strict confidentiality.

Skills Required

  • MSc in Machine Learning, Computer Science, Statistics, or directly related quantitative field from KTH, Chalmers, ETH Zurich, EPFL, or equivalent institution
  • Deep theoretical and applied understanding of recommendation systems with implementations shipped to real users
  • Experience with sequential modeling applied to user data (recurrent architectures, attention-based sequence models)
  • Production ML engineering discipline: build reliable production code and systems (not just experiments/notebooks)
  • Demonstrated ability to take a recommendation system from research to production
  • Fluency in written and spoken English
  • PhD in Machine Learning, Computer Science, or related quantitative discipline
  • Research depth in recommendation, sequential modeling, or online learning
  • Experience with privacy-preserving approaches to behavioral modeling
  • 2-5 years of experience (exceptional graduates assessed equally)
  • Body of independent work: published research, open source contributions, or personal projects
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The Company
Year Founded: 2026

What We Do

Q Innovations is a Stockholm-based, well-funded early-stage technology company developing proprietary, patent-pending technology to redefine how people search, discover, and transact. Its user-first approach treats people as active participants, allowing their data to work for them while retaining control. The company is building AI-driven personalization and a novel engine and system to improve digital discovery and transaction experiences.

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