Machine Learning Engineer

Salary Competitive

Do you want to join a fast-paced startup and work on the bleeding edge of recommendation systems, where your work immediately impacts the user experience for millions of users? We are looking for a Machine Learning Engineer who thrives in a fast-moving environment.

What you will be building:

Recommendation systems that are guaranteed to always show relevant recommendations for any scenario and that are easy to maintain, deploy and improve.

We need someone who we can count on to:

🙋 Own: Improve our recommendation systems for new and existing customers, proactively take decisions on what data we should collect and use, as well as creating and improving tooling to deploy, introspect and monitor our recommendation systems to constantly make them better.

💻 Teach: What's possible and what's not possible? Tradeoffs between iteration speed and complexity of ML models. How to build a system that is easy to debug and that fails gracefully if there would be changes in the input data.

🎒 Learn: Our deployment workflows, data engineering best practices, efficient architecture for realtime inference.

🚴 Improve: Model performance, iteration speed, and robustness of our entire system, to delight the end-users of our product.

Desired skills and qualities

  • Deployed applications using cloud platforms or your own servers

  • Experience working in a mature data engineering codebases

  • Administered and interpreted A/B tests

  • Taken product decision

  • Proficiency with cloud computing (AWS, GCP, or Azure)

  • Used Docker, Kubernetes, Argo or similar

We expect you to:

  • Be proficient in Python

  • Have experience with data wrangling

  • Have a collection of CLI tools and tricks and Linux/Unix know-how that you are comfortable with

Within 1 month we expect you to:

  • Understand the ins and outs of how our current recommendation system, data pipelines and recommendation API works

  • Have been in several customer calls understanding how they perceive our product

  • Get to know the team and learn from and collaborate on directing the product development forward

  • Configure, optimize and measure the performance for newly onboarded e-commerce stores

Within 3 months we expect you to:

  • Significantly improve the quality of recommendations using more facets of data impacting the user experience on > 50% of our integrated customers

  • Generalize our system further, ensuring that recommendations for new customers are perfect from day 0 without needing much fine tuning

  • Improve our codebase such as adding tests and monitoring of our recommendation's performance

Within 6 months we expect you to:

  • Lead focused projects to improve our product further, e.g. relating to model performance, deeper personalization, knowing users across platforms, etc

  • Have ownership of one or multiple areas in a rapidly growing company

  • Ensure our codebase supports adding new features in a seamless way

Within 12 months we expect you to:

  • Architect new product features and lead the implementation

  • Contribute to the vision and long-term strategy of our company and product

  • Start exploring ways to increase our product offering outside of just product recommendations

Perks and benefits

This job comes with several perks and benefits

Free coffee / tea
Free coffee / tea

Get your caffeine fix to get you started and keep you going.

Flexible working hours
Flexible working hours

Time is precious. Make it count. Morning person or night owl, this job is for you.

Social gatherings
Social gatherings

Social gatherings and games; hang out with your colleagues.

Near public transit
Near public transit

Easy access and treehugger friendly workplace.

Equity package
Equity package

Want to be a partner? Look no further.

Free friday beers
Free friday beers

Friday is something special, let's enjoy a beer together.

See all 11 benefits

Working at is building AI-driven product recommendations. Increasing revenue for your store We use neural networks, computer vision and natural language processing to understand your products. This way we can start making high-quality recommendations immediately. Why do our recommendations work so well? Most recommendation systems use past transactions as their main source of data. We know better. Our system mimics the way humans understand products so that we can start showing relevant recommendations in your e-commerce store from day one.

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