We Are

At RelationalAI, we are enabling the future of data-centric systems that learn, reason, and predict, built on our cloud-native relational knowledge graph management system. We are bringing together a global team of exceptional people who inspire and respect one another to achieve our mission.

We are intellectually curious, with problem-solving mindsets and a proven track record of delivering results. We value the ability to learn, grow, and respond to an ever-changing landscape of challenges and opportunities as much as we value past experience.

RelationalAI is a remote-first company that has been operating with a distributed team since day one.  We are based on six continents, support team members wherever they live, and are fluent in the tools and processes of distributed and often asynchronous work.

About the Research Team 

The research team is responsible for developing algorithms to make our product as expressive, intelligent, and efficient as possible. Our research centers on:

  • Efficient exact and approximate inference over structured domains using an algebraic approach that permits a unified view on logical and probabilistic inference.
  • Novel applications of this fast inference to problems in machine learning, optimization, constraint satisfaction, probabilistic programming, and database querying.

An extensive network of academic collaborators ensures our work is always state-of-the-art and addresses important real-world problems. 

The Opportunity

Advanced algorithms form the core of our product and provide state-of-the-art prediction and decision-making informed by current data and deep domain knowledge. Our approach blends mathematical optimization, statistical-relational AI, logic, and fast inference in structured models. If you love algorithms and fast implementations, you’ll love this role.

As an algorithms research engineer, you will work as part of the research team, which is responsible for ensuring our product is always as smart and fast as possible.

For example, the research team works on:

  • Exploitation of domain knowledge in the form of knowledge graphs (graphical models) to accelerate (discrete and continuous) optimization and constraint satisfaction.
  • State-of-the art exact and approximate inference in graphical models.
  • Exploiting rich knowledge graph representations for: 
    • Data-efficient supervised and generative learning.
    • Causal reasoning and explainable AI.
    • Monte Carlo methods for simulation and inference in probabilistic programming.

Our RKGMS and algorithms are written in Julia, a high-level language built for technical computing that makes it simpler to build research prototypes that are fast. It’s not a problem if you don’t know Julia; we have a large team of developers and lots of experience. 

What You Will Do

On our quickly growing team, we are looking for people excited to help realize cutting-edge learning and reasoning.

As a part of the team, you will:

  • Implement the latest research from the academic literature, benchmark and improve upon these methods, and adapt them to real-world use cases.
  • Integrate research ideas with our KGMS to test ideas at scale.
  • Prototype original research in compiling declarative problem descriptions to representations suitable for efficient solution by dedicated solvers (e.g. MIP, SAT, SMT, ASP).
  • Interact with our research network at the frontier of statistical relational AI, optimization, and database theory.

Who You Are & What You Bring

There are four absolute must-haves: you're excited about algorithms and making them fast; you’re in awe of the potential of AI and optimization to provide unsurpassed decision-making; you're curious and enjoy learning; and you’re a team player — you thrive in a collaborative environment where customer success is more important than individual success.

Your qualifications may include:

  • Having a background in optimization or ML and being interested in how traditional methods can be improved with domain knowledge in the form of knowledge graphs or graphical models.
  • Having successfully implemented a cool AI or optimization algorithm you were interested in and having a consuming interest in the math that makes these methods work.
  • Having a research background and wanting to dive into applications. You're up to date with the latest papers and would love to find a place where you can implement novel methods in a collaborative and encouraging environment.

If this sounds like something you would enjoy, we encourage you to apply. We can't wait to hear from you!

Why RelationalAI

RelationalAI is committed to an open, transparent, and inclusive workplace. We value the unique backgrounds of our team. We are driven by curiosity, value innovation, and help each other to succeed and to grow. We take the well-being of our colleagues seriously, and offer flexible working hours so each individual can find a healthy balance that affords them a productive, happy life wherever they choose to live.

Country Hiring Guidelines:

RelationalAI hires around the world. All of our roles are remote; however, some locations might carry specific eligibility requirements.

Because of this, understanding location & visa support helps us better prepare to onboard our colleagues.

Our People Operations team can help answer any questions about location after starting the recruitment process.

 

Privacy Policy: EU residents applying for positions at RelationalAI can see our Privacy Policy here.

California residents applying for positions at RelationalAI can see our Privacy Policy here

 

RelationalAI is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.

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