Principal Software Engineer
About the job
We’re looking for a top-notch engineer, with plenty of hands-on software engineering experience, along with an algorithmic mind. You will be a core part of our R&D team, which is composed of experienced (and extremely talented) Software Engineers and Data Scientists.
In this role, you will work on enabling ongoing connections with customers’ data, accessing and analyzing TBs of data on a daily basis, and efficiently implementing complex algorithms that run on an ongoing basis. This role will provide you with unique and unparalleled experience with machine learning in production since you’ll be working intimately with the ML pipelines of numerous different companies across various verticals.
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.
What you will do
- Mentor a team of talented engineers across their work with every part of our tech stack
- Design and implement the infrastructure of the system including the Ingestion, Algorithms, performance optimizations, and interactive applications
- Architect and build a highly scalable data platform for diversified and complex data flows
- Have end-to-end ownership: Design, build, ship, measure, and maintain our backend services
- Be the main owner for the architecture combining our open-source with our commercial offering
Who you are
- +7 years in software development – Python, Go
- at least 2 years as a technical lead/software architect
- Experience in designing and maintaining large-scale applications
- Deep familiarity with cloud infrastructures and architectures – specifically AWS
- Experience designing with both NoSQL and relational databases.
- Deep familiarity with serverless and microservice architecture
- DevOps skills such as Docker, Kubernetes, Cloudformation, Terraform, etc. – advantage
- Deep knowledge of Python – advantage
- Experience with open source – advantage
- BSc in computer science or equivalent
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.