Surge Hq

We are seeking a highly adaptable and results-oriented individual to join our team as a Senior Growth Lead. In this role, you will be responsible for driving growth initiatives across contributor recruitment, user reactivation, and new audience acquisition for Surge AI. You will work collaboratively across various functions, bringing structure to complex challenges and achieving significant outcomes with minimal supervision. This is an opportunity to own critical growth areas end-to-end, from strategy development to execution and iteration, in a fast-paced environment.

Surge is building a platform that powers the most powerful AI models in the world, in partnership with leading AI companies. Our mission is to advance AGI by integrating human intelligence and creativity into the data that shapes AI systems. We combine elite human expertise with cutting-edge tools for scalable oversight, from building rich RL environments to conducting rigorous evaluations. As a Research Scientist focused on data, you will work at the forefront of LLM development by designing datasets that influence how frontier models behave. You will collaborate directly with AI research teams to explore new data collection strategies, assess dataset quality, and discover insights that enhance alignment, safety, and model performance. This is a research-driven, impact-heavy role where you can rapidly test ideas in live production environments, influence data-centric methodologies for top AI labs, and help define how high-quality data drives the next generation of AI systems.

Surge is building a platform to power the next generation of AI, partnering with leading AI labs and frontier model teams. As a Forward Deployed Researcher, you will embed directly with these teams to understand how their models behave in real-world applications and ensure their reliability at scale. This role involves leading hands-on research to evaluate model capabilities, identify subtle failure modes, and design interventions that improve model behavior and deployment outcomes. You will uncover insights that inform future model development, working collaboratively to turn complex real-world challenges into actionable breakthroughs.

Surge is seeking a Software Engineer to focus on Coding Evaluation & Training Data. This role is at the intersection of software engineering and product, responsible for building and running systems that train frontier models to code. You will own end-to-end coding data projects for top AI labs, designing tasks, RL environments, and evaluation schemes that mirror real-world software engineering practices. This is an ideal opportunity for a software engineer passionate about training data quality, agentic evaluation, and system design involving humans, models, and tools, rather than traditional product feature development.

As a Research Engineer, Coding Evaluation & Training Data at Surge, you will be at the forefront of developing systems that train frontier models to code. This role blends software engineering and product development, focusing on creating and managing the processes that teach AI to perform real-world software engineering tasks. You will own coding data projects from conception to completion, designing tasks, environments, and evaluation methods that accurately reflect the complexities of software development. This is an excellent opportunity for a software engineer passionate about training data quality, agentic evaluation, and system design involving humans, models, and tools.

Surge is building a platform to power the next generation of AI, aiming to achieve AGI by combining human intelligence and creativity with cutting-edge tools. As an RL Environments Architect, you will be responsible for designing, instrumenting, and governing the simulated worlds where AI agents learn. This involves defining the core components, reward structures, interfaces, and telemetry that ensure faithful, stable, and scalable training signals. You will also establish standards for data quality and reproducibility across large-scale agent environments, focusing on simulation fidelity, coverage, and failure surfaces to create learnable curricula that drive safe and rapid progress in agentic systems.