Relari is defining the new category of data-driven development infrastructure for GenAI applications. We work with top AI teams from leading startups to large enterprises, and we are growing at a blazing-fast rate. Our founding team are experts on AI system production and we are well-funded by top-tier VCs.

As a founding AI engineer at Relari, you will solve the most critical challenges facing our society’s adoption of foundation models.

In this role, you will:

• Define and spearhead the development of core features for a state-of-the-art Generative AI Testing and Simulation stack.
• Regularly innovate and conduct novel research to push the boundaries in LLM evaluation, LLM fine-tuning, synthetic data generation, and many other fields.
• Experiment with the latest findings in AI research and proactively incorporate best industry practices into production.
• Collaborate directly with customers and partners across the AI ecosystem.

You’re a good fit if:

• You’re a Software Engineer, Machine Learning Engineer, or Research Engineer at FAANG or a fast-growing software company.
• You’re a Research Scientist at a leading academic institution or top industry research group.
• You thrive in a fast-moving environment and view unstructured environments as opportunities to identify the most impactful work and define the future success of the company.
• You are ownership-minded. You don’t wait for company processes to get work done; instead, you proactively contribute wherever you can to drive success.

Bonus points if you have:

• Shipped an AI product or have experience training and fine-tuning large foundation models.
• Published and reviewed papers in academic conferences (NeurIPS, ICML, ACL, etc.).
• Contributed to our open source project.

Our testing and simulation stack leverages a suite of foundation models and traditional machine learning techniques to generate large-scale, high-fidelity synthetic data for rigorous evaluation and systematic fine-tuning. There's a heavy research angle to our tech stack as we are on the cutting edge of defining the best practices in building reliable AI applications. We collaborate with top institutions like MIT to publish research papers and open source part of our stack (continuous-eval).