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