Forward Deployed Researcher

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 of AI. Our platform powers the most capable models in the world, in partnership with the AI models you use every day.

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.

As a Forward Deployed Researcher, you’ll embed directly with leading AI labs and frontier model teams to explore how their models behave in the wild — and what it takes to make them work reliably at scale. You’ll lead hands-on research to evaluate capabilities, surface subtle failure modes, and design interventions that shape model behavior and deployment outcomes.

You won’t just run tests — you’ll uncover insights that inform the next generation of model development. This is a role for someone who thrives on close collaboration, lives for iteration, and loves turning real-world complexity into actionable breakthroughs. Your work will shape how some of the most advanced AI systems interface with the world.

• Curiosity about Model Behavior – Interest in how models reason, generalize, and fail when applied to complex real-world tasks

• Experimental Rigor – Strong instincts for research design and insight generation in ambiguous settings

• Customer-Centered Mindset – Excitement to embed deeply with partners, understand their goals, and build towards meaningful impact
  

• Design and run evaluations to probe model generalization and behavior under deployment constraints

• Build data pipelines, fine-tuning workflows, or interventions to steer and improve model outputs

• Partner with internal stakeholders and external AI labs to operationalize custom frontier model use cases

• Explore and address failure modes in long-context reasoning, tool use, or real-time feedback loops