Deepchecks | Machine Learning Researcher

Who we are

Deepchecks is a VC-backed startup tackling the huge problem of controlling Machine Learning systems.

AI systems are being adopted by more and more organizations and are taking an increasingly important role in their business. Although many resources are allocated to creating and optimizing the machine learning models, they still lack “common sense” and make various mistakes that may go undetected for long periods of time.

We focus on detecting, preventing, and fixing these “AI glitches”, using mathematical concepts and algorithmic research. Our product monitors these systems in production, identifies a wide range of potential problems, and offers different types of alerts and explanations (depending on the type and the severity of the issue).

The startup was founded by two Talpiot graduates / Data Scientists and a leading professor in this field. Following a few months of R&D and initial customer traction, the time has come to expand our extremely talented (and fun!) team. Our offices are in Tel-Aviv, although we’ve recently been working from home most of the time.

We’re looking for a top-notch Machine Learning Researcher that has both broad experience with Data Science (i.e. experience with various types of models and tasks), and solid coding skills. We’re creating unique methods for determining when models can/can’t be trusted and automatically tackling issues such as: Prediction confidence, overfitting, model monitoring, concept drift, model  explanations, and more.

In this role, you will be in charge of developing these capabilities for the Deepchecks Open Source package and for the Deepchecks Pro product, most of which have no “textbook solution”. You will be developing algorithms for a wide range of models and data domains, from tabular to Vision and NLP and will help create robust tools for detecting generic issues with ML model and pipelines. A core part of the role is also the unique opportunity to shape and direct our product from a user’s point of view.

We’ll be going through a lot together, so we’ll want your character and mindset to be a good fit for a fast-moving startup.

Required Experience:

• M.Sc./Ph.D. in a quantitative field or at least one Kaggle gold medal
• At least 3 years of industry experience in ML Research / DS roles
• Proven track record of excellence in machine learning
• Proficient in Python with a strong emphasis on rapid exploration and evaluation of various algorithms and approaches. Demonstrated ability to effectively test and adapt solutions to diverse use cases
• Product-driven mindset, and ability to contribute to the prioritization process and directly interact with the end users
• End to end skillset – able to own features from an idea stage through design, coding, iteration, user feedback and owning the implementation

Advantage if Experienced with:

• Owning and improving deployed ML systems
• MLOps or AutoML tools (either on the vendor or the customer side)
• XAI, model robustness or model monitoring
• Unsupervised learning