Interviewing at Robust Intelligence

Thank you for interviewing with us at Robust Intelligence! Below, you can find some information about our company, the interview process and what we look for in candidates. The team is very much looking forward to learning more about you soon!


Robust Intelligence was co-founded by Professor Yaron Singer and Kojin Oshiba in 2019 after years of robust machine learning research at Harvard University. The pair realized the significant gap between the state of machine learning and how data scientists use it in practice. They set out to solve the chronic problem of machine learning model failure. Since the journey began, the team has attracted exceptional talent working at the bleeding edge of AI from companies including Amazon, Google, Lyft, Meta, Microsoft, Quora, Salesforce, and Uber.

You will meet many of our teammates throughout your interview process.

We're backed by an exceptional team of investors: Sequoia Capital (lead investor), Tiger Global, Engineering Capital, Harpoon, In-Q-Tel, Ram Shriram (seed investor in Google) and Alex Balkanski (general partner at Benchmark Capital).

Why we exist

AI is eating the world, but the ML models used to make business-critical decisions are prone to failure. This introduces risk to businesses and end-users. Data science teams today react to changes observed in dashboards and write ad hoc tests. Robust Intelligence is on a mission to proactively eliminate model failure by instilling ML Integrity.

The Robust Intelligence platform proactively eliminates AI failure at every stage of the ML lifecycle, from training the model in development to protecting models in production.

Pre-deployment: AI Stress Testing provides hundreds of auto-configured and customizable tests that can identify implicit assumptions and model failures, allowing you to harden against these vulnerabilities.

Post-deployment: AI Firewall protects production models from data that can cause erroneous predictions and failure. AI Continuous Testing monitors the behavior of models in production to identify issues, informs you when it’s time to retrain a model, and automates root cause analysis of model failure.

The transition to AI is one of the most exciting technological revolutions of our time, but models are still brittle and rife with vulnerabilities. Robust Intelligence's mission is to eliminate AI failure by instilling ML Integrity.

Throughout the interview process, we will introduce you to various aspects of the platform in order to give you a sense of our end-to-end solution and how it serves our mission.

AI Firewall

Protects production models against attacks, operational and ethical threats, and undesirable responses via an API that validates inputs and outputs in real time.

Continuous Validation

Ensures AI models and data are validated as part of the AI lifecycle by surfacing weaknesses via automated red teaming, establishing a standardized audit record for their health, and tracking adherence to standards and regulatory compliance.

I am interviewing for


Engineering at Robust Intelligence

Our engineering team is on a journey to build a product that redefines how machine learning systems are built today. We take pride in the quality of the product we ship and providing customers value.

On the software engineering side, we are building the Robust Intelligence Platform that is robust, insightful, easy to use and integrates seamlessly with the many existing AI platforms in use. This presents the exciting challenge of transforming our core AI technologies into a secure, scalable, and intuitive product that can be delivered to customers with production AI systems.

On the machine learning side, we are developing and productizing cutting edge technologies for discovering and then fixing security, fairness, and robustness issues of machine learning models and the data that flows through them. This requires a strong understanding of ML fundamentals and engineering discipline, coupled with a healthy dose of creativity and a lot of collaboration.

Our engineering team is collaborative and intellectually curious. Many engineers work cross-functionally between software, machine learning engineering and product to achieve our goals.

Engineering Tools:

Frontend: React, Typescript
Backend: Golang
ML: Python, Pytorch
Cloud: GCP

Tech Stack:

Source Control: Github
CI/CD: CircleCI
Sprint management: Monday
Technical documentation: Notion

Hiring philosophy

We are looking for exceptional employees who are not only technically strong, but are excellent team players. Everyone who joins the company today shapes the culture of the company in ten years. We value members that are humble, genuine, and fun to work with.

We look for employees who thrive in an early stage startup environment. This type of environment is not everyone’s cup of tea. We look for employees with grit, ready to immerse themselves in a dynamic and fast-paced atmosphere. Our vision is what unites us during this startup marathon with highs and lows. We look for employees who are truly passionate about how building robust and secure AI systems shape the future of AI.

Inclusion is our biggest asset. Our customers can rely on us because we rely on our team’s unique backgrounds and experiences to write software without blind spots. We believe in the importance of building a safe and diverse community, where we embrace people who aren't afraid to challenge the status quo.

Interview process

Step 01


This is usually a 20-minute conversation with either the recruiter or hiring manager. It’s an opportunity for you to learn more about the role and we’ll ask some questions about your experience and your interest in Robust Intelligence.

Step 02

Technical screen

This is a one-hour, technical interview with one of the members in our engineering team.

Step 03

Team interviews

We want to create the opportunity for you to meet as many future co-workers as possible. This way, you can get to know the people you could be working with, and the problems they’re currently solving. You can expect to meet between 3-5 people for up to an hour each. The interviews will be a combination of technical and behavioral in nature. Technical interviews will have a systems design, coding, and algorithm component.

Step 04

Leadership interviews

This is is an interview with our CEO and/or Co-founder. This conversation is where you have an opportunity to get to know and understand more the vision and mission of the company, our values, our growth potential, and why we are all so excited to work here. It is also an opportunity for you to ask questions about anything that you think are relevant and important.

Step 05


Collecting diverse feedback from our team members of varying levels of seniority, experiences, and perspectives is an important part of how we work. Your hiring team will take the time to meet, share individual interview feedback and their objective view on the hire/no hire decision. Once the decision is made it will be communicated to you by the recruiter or hiring manager.

Step 06


For candidates that move to the Offer stage, we put together a comprehensive offer package that includes a competitive base salary, equity, and benefits. We invite you to socially meet with other members of the team that you might not have had the chance to meet during interviews. This social interaction helps you to get to know us better. Our team is also available to share advice, best practices, and thoughts on transitioning from your current position to your new role at RI.

Step 07


Once you have accepted our offer, completed the paperwork, reference checks, and background check we will share onboarding information that will help you on your first day as a new member of Robust Intelligence!


If I'm interviewing for a software engineering role, do I need to to know any machine learning?

Prior machine learning experience for software engineer candidates is not required. In fact, most of our software engineering members have not worked on ML systems in the past, but have made tremendous contributions to building out our platforms.

If I'm interviewing for a machine learning engineering role, do I need to to know any software engineering?

Yes. Our MLE's write production code. To that end, we expect MLE candidates to have a solid understanding and practices of software engineering principles.

What should I expect in technical interviews?

Technical interviews will have a systems design, coding, and algorithm component. MLE candidates will be tested on ML and SWE skillsets. As noted above, we will not test SWE candidates on ML knowledge.

How long does the interview process take?

We try to schedule our interviews to facilitate candidate timelines. The entire process generally take 2 - 3 weeks.

Are all the interviews going to be remote?

Currently our default is to conduct interviews remotely. Anyone who is interested is welcome to interview with us in person in our SF office. If you'd be interested in interviewing in-person, please reach out to Riffat at for details and necessary precautions.

Who should I reach out to for questions about the interview process?

Please reach out to Riffat at

Where are you located?

We are located in the Dogpatch area in San Francisco. Our office address is 1400 Tennessee Street, Unit 4, San Francisco, CA 94107.

Our second office is located in Menlo Park at 724 Oak Grove Ave Menlo, CA 94025.