Built by real life builders

The team behind Qwak understands your ML challenges.

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The story behind Qwak

We are a founding team of Software and Machine Learning Engineers and Product Designers.

In our roles at Payoneer, Amazon, ironSource, and Wix.com we saw that machine learning was growing in importance for the mission of our companies. We saw that designed, built and deployed correctly, we could deliver smarter, more valuable solutions to our customers.The breakthroughs we were seeing were significant, and so the desire grew to add more talent and ML capabilities to our solutions.

In our quest to scale our team and output,

we ran into a number of challenges:

1
Expensive

Experienced data scientists and ML engineers are hard to find and expensive to hire.

2
Lacking tooling

The tools and solutions to manage the lifecycle from model and features design through testing, deployment, monitoring and automating are lacking.

3
Disconnected approach

The ad-hoc, disconnected approach to ML operations causes serious frustration, wasted time, and severely constrained throughput.

As such, our ability to move from model design to model implementation was constrained.

In a quest to improve this critical capability, we first looked for a platform that we could implement to accelerate our ML throughput. After striking out in this endeavor, we undertook to develop our own internal system.

After seeing the benefits we were delivering, we asked the question, how are other companies solving this problem?

We found that very large companies with sophisticated ML capabilities had done what we had done - build a bespoke internal solution for ML Engineering.

What about everyone else?

As we started talking with more and more companies that were ramping up their use of ML, we found they were struggling with the same problem.
For them, the impact was significant: An inability to quickly take ideas, build ML models, and run them as part of their product in any reasonable time frame.

So, we got together...
left our respective companies, and set out to build the ML Engineering platform for the rest of us

Speed

Companies where ML is increasingly important, and therefore speed to production is increasingly important too.

Core Competencies

Companies that want to focus on their core competencies, and don't want to spend their precious resources building and maintaining an ML Engineering Platform.

Throughput

Companies that want more ML throughput, even with a scarcity of ML data scientists and ML engineers.

The Team

Alon Lev
CEO
VP Data & Site Manager at Payoneer

Head of DB Infrastructure at Mamram unit, IDF
Yuval Fernbach
CTO
EMEA Machine Learning Specialist at Amazon Web Services

CTO at Mamram unit, IDF
Lior Penso
COO
Business Development Manager at Amazon Web Services

Business Development at IronSource
Ran Romano
VP ENGINEERING
Data & Machine Learning Engineering Manager at Wix

Software Engineer at VMware

Open Roles

Interested in becoming a part of our team?

View open positions

Backed by

StageOne

NATE MEIR

StageOne invests in founders of Early-Stage B2B Deep Tech startups in the Israeli ecosystem. The firm's investment team leverages networks across the Israel - US axis to help its portfolio companies become market leaders in their respective fields.

Amiti Ventures

MODI ROSEN

Amiti’s Partners seeded and helped scale Israel's best tech teams, among them Innoviz, Waze, AppsFlyer, Valens, Vayyar, Cycognito, Aidoc, NextSilicon and many more.

Leaders Fund

GIDEON HAYDEN

Leaders Fund is a Toronto based VC that invested in some of the world's leading startups like; Ada, Drata, Snyk, CallRail, Spot and many more.

Our Offices

Tel Aviv
San Francisco