Software Engineer (SWE)

AI Talent Now; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

AI Talent Now in San Francisco is looking for an engineer to take ownership of projects that innovate AI evaluation techniques. You will design and build scalable systems, collaborate with various teams, and ensure code quality through reviews and documentation.

This role involves a variety of tasks, from prototyping new RL environments to enhancing existing systems, providing an exciting balance of challenges in the fast-paced AI sector.

Qualifications

• Experience with reinforcement learning (RL) environments.
• Familiarity with Kubernetes for deploying distributed experiments.
• Skilled in building APIs and human-in-the-loop platforms.

Responsibilities

• Design and build scalable systems for RL environments and APIs.
• Collaborate with teams to ship features quickly.
• Write clean, maintainable code and assist in documentation.

Job description

San Francisco, United States | Posted on 06/15/2026

AI Talent Now, LLC is a powerhouse in direct-hire talent acquisition, connecting exceptional talent with industry-leading organizations across IT, Engineering, Financial Services & Fintech, and Manufacturing & Robotics. Headquartered in Atlanta, Georgia, we proudly serve clients and candidates nationwide.

Job Description

AI Talent Now Job #ZR 71

About this role

As part of this dynamic engineering team, you’ll have end-to-end ownership over projects that push the frontier of AI evaluation. This is not a narrow role.

One week you might prototype a new RL environment from a research paper, the next you’ll be deploying distributed experiments on Kubernetes, and the week after you’ll be improving the reliability of our Next.js dashboards or building a Kafka pipeline for annotator analytics.

What You’ll Do

Design and build scalable systems: RL environments, APIs, and human-in-the-loop platforms.

Collaborate across research, product, and design to ship features quickly.

Write clean, maintainable code and contribute to documentation.

Participate in code reviews and design discussions

Solve real-world scalability and reliability challenges.

Contribute to the core infrastructure powering data and evaluation for leading AI labs.