Over the last few decades, technological innovation has relied on democratizing some of its key ingredients: knowledge (open publishing platforms), algorithms (code repositories) and computing (cloud providers). However, the last key component, data, largely remains trapped behind barriers of regulation, privacy, schema standards and competitive risk. Enabling scalable access to data will unlock enormous value for both individual developers and companies. This has only been made possible in the last few years, through advances in manifold learning algorithms and privacy-preserving computing. See our blog for more details.
One of the most valuable kinds of data today is in the financial sector. Financial data plays a key role across many industry verticals. However, it has notoriously been locked behind regulatory barriers and a lack of format standards. Our first product is an API that makes financial transactions accessible with minimal engineering overhead to both humans and machine-learning models in a scalable and privacy-preserving way.
We – are allergic to over-engineering. – are anarchists at heart and like to hack around the status quo. – are radically honest and appreciate challenging one another, rather than giving out “pats on the back”. Yet, we can always rely on each other for support, feedback and results. – are willing to learn and adapt quickly to new situations and requirements. Languages, frameworks, libraries, compilers, etc. are just tools for a job. A new problem might need a new tool. If it doesn’t exist yet, we will build it. – have a sense of humor (well, we think we do).
As an early member of our team, you will help – push the limits of cutting-edge computing and machine learning – build the team and drive the direction of the company. – shape our product and culture. – experience the direct impact of what you build on hundreds of millions of people’s financial lives.
The following is a big plus – fluency in Python and Rust – experience with multi-GPU environments – recognized open-source contributions – participation in competitive programming events (CTFs, IOI, IMO, Topcoder, Kaggle, etc.) – experience with infrastructure-as-code, cloud deployments, Kubernetes, containers
FAQ
What will be my role?
We are mostly looking for ML and backend engineers at the moment. However, if you are excited by the vision and believe you will be a good fit, we would love to talk!
What if I recommend someone who ends up getting hired?
We will give you 20k USD for the referral.
Where is Ntropy located?
We are fully remote, with a virtual base in San Francisco, CA.
What time-zones do you work with?
We hire anywhere in the time zones GMT-7 to GMT+1.
Do you consider part-time work?
Not at the moment. Full-time roles only.
How are you funded?
We are backed by some of the top fintech investors in the world. Have raised single-digit millions of dollars so far. Can share more details over the call.
Do you already have a product?
Currently in beta with 20+ customers.
Do you plan to sell customer data?
Ntropy is on a mission to enable products without data barriers. We will never sell customer data and will always put privacy and customer benefit ahead of any auxiliary financial gains.
What is the interview process like?
Send us problems you have solved before and how. Please include as much detail as possible: code, algorithms, derivations, proofs, etc. We will then do a video call to kick things off and go through it (45 mins).
We will give you a take-home project related to whatever we are currently working on (3-4 hours). Alternatively, if you have a relevant project that you worked on previously that demonstrates your skills as an engineer, you are welcome to use that instead.
We will then do a deep-dive through the project over a call and discuss the implementation, improvements and bottlenecks.
Above all, we respect your time and commitment and will keep you up to speed on where we are at during the whole process.
What are your hiring plans?
We aim to be 15-18 people in the next 6 months. Mostly engineering roles.
What is your current stack?
back-end – Python + Rust (where performance matters) compute – AWS + Azure ML – PyTorch + ONNX infra – Redis (cache), RDS + S3 (storage), EKS + Lambda (auto-scaling), EC2 4xGPU nodes (training)
Work / life balance?
At the current stage of the company, our probability of success is directly correlated to effort. The number of hours you put in can make a big difference. However, if we cannot rely on your presence at all times, we must surely be able to rely on your outcomes, no matter how much time it takes. Mental and physical well-being are above anything else.
What is the compensation?
$130k – $180k • 0.3% – 1.0%
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