Senior Software Engineer - Pairwise Evaluation

Build production AI infrastructure

Design and maintain backend systems that support AI training and deployment. This is a software engineering role focused on production services, reliability, and integration with machine learning work.

• Develop scalable backend systems and write clean, tested production code in languages such as Python, Go, or TypeScript.
• Design and optimize APIs, data pipelines, and microservices for performance and reliability.
• Work with machine learning engineers and researchers to integrate models into production environments.
• Investigate distributed systems failures and identify underlying causes.
• Contribute to architecture decisions, code reviews, and engineering practices.
• Work across infrastructure, DevOps, and frontend integration when needed.
• Manage priorities and deliverables independently with limited supervision.

Core engineering background

At least five years of professional software engineering experience is required, with a record of delivering production systems. You should have deep proficiency in a modern language such as Python, Go, Java, TypeScript, or Rust.

The role requires a strong understanding of distributed systems, cloud infrastructure such as AWS, GCP, or Azure, and containers such as Docker or Kubernetes. Experience with REST APIs, data pipelines, or event driven architectures is also important.

You should be comfortable with relational and nonrelational data stores, including PostgreSQL, Redis, MongoDB, or similar technologies. Clear written documentation, strong problem solving, and reliable independent delivery are essential in an asynchronous environment.

Helpful technical experience

Experience with model serving, training pipelines, vector databases, or large language model integration would be valuable. Other useful skills include continuous integration and delivery, Terraform or Pulumi, observability, and high throughput or low latency systems.

Open source contributions or substantial personal projects can demonstrate engineering depth. Experience with distributed teams, natural language processing, or AI safety and alignment workflows is also relevant.

Contract and hours

This is an hourly remote contract with a commitment of 20–40 hours per week and a flexible schedule. There may be ongoing work or longer contract extensions as additional initiatives begin.