Machine Learning Research Scientist
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 OpenAI, Anthropic, Meta, and Google.
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 Machine Learning Research Scientist, you’ll help shape how the world’s most advanced AI models are trained, aligned, and evaluated. You’ll work on highly practical problems at the intersection of research and deployment — from designing model-in-the-loop data pipelines, to evaluating frontier LLMs, to prototyping new methods for data-centric RLHF.
You’ll collaborate closely with our internal engineering team and our partners at top AI labs, turning research ideas into systems that improve real-world model behavior. This is a role for someone who wants to move fast, stay close to production, and make research that matters.
What We’re Looking for
- Deep Understanding of Machine Learning – Experience with large models, generative AI, or alignment methods
- Scrappy, Outcome-Oriented Mindset – Comfortable working in ambiguous spaces, shipping quickly, and refining as you go
- Collaborative Instincts – Excitement to partner with world-class researchers, data scientists, and engineers
- Interest in Human Feedback Systems – Enthusiasm for using human-in-the-loop methods to shape model behavior and safety
What You'll Do
- Prototype a new red-teaming workflow using LLMs as adversarial agents
- Design reward model experiments and run offline evaluation pipelines
- Explore methods for filtering or structuring model training data to improve safety and robustness
How to Apply
To apply, please email [email protected] with a resume and 2-3 sentences describing your interest in Surge. We love personal projects and writings too!
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