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AI Summary
About Us
Our mission is to raise AGI with the richness of human intelligence — curious, witty, imaginative, and full of unexpected brilliance.
Surge was founded by engineers and researchers who dreamed of building the next generation AI. We're building a platform that powers the most powerful models in the world in partnership with companies like Anthropic, Google, Microsoft, and Meta.
At Surge, we believe the path to AGI isn't just about scaling compute—it's about embracing the unlimited ceiling of human intelligence and creativity in the data that shapes these systems. Our platform combines elite human expertise with cutting-edge tools for scalable oversight, from building rich RL environments to conducting rigorous evaluations that go beyond benchmarks. We've run a profitable business from day one without raising venture funding.
The Role
As a Forward Deployed Engineer, you’ll work directly with top AI labs to design and ship the infrastructure behind their most critical data workflows. You’ll embed with cutting-edge research teams, understand their model challenges, and build the pipelines, tools, and automations that move their experiments forward.
This isn’t a heads-down backend role — you’ll be on the frontlines, blending engineering with problem-solving, and translating ambiguous research needs into high-leverage technical systems. Your work will directly shape how the world’s most advanced models are trained and evaluated — and you’ll ship fast, iterate constantly, and see the impact of your work in real time.
What We’re Looking for
Passion for Building Tools – Developing robust, scalable systems to accelerate AI data workflows
Interest in Applied ML – Curiosity about how models work and excitement to build infrastructure that drives model improvements
Love of Collaboration – Desire to work hand-in-hand with top AI labs, shipping engineering solutions that make an immediate impact
What You'll Do
Design custom data pipelines and integrations to collect and process high-quality training and evaluation data
Build technical infrastructure for advanced quality control workflows, including model-in-the-loop and human-in-the-loop systems
Develop tools to automate experiments that improve model safety, interpretability, and human alignment
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