AI Researcher
V
VettoAI Research
Global / RemoteFull-Time
SalaryCompetitive compensation and a stock option plan.
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Job Details
- Required Skills
- PythonData AnalysisMachine Learning
Requirements
- Experience doing empirical research or similarly open-ended technical work with machine learning systems.
- Hands-on experience designing and running agentic systems for complex work, such as coding agents and automated research pipelines.
- Good experimental judgment: you can form a hypothesis, choose useful baselines and metrics, run a careful experiment, and make sense of noisy results.
- Strong Python and data analysis skills, plus enough software engineering ability to prototype your own research workflows.
- The independence to take an ambiguous question about a model and turn it into a dataset, benchmark, experiment, or other concrete contribution.
- Rigorous analytical thinking — you look at the underlying evidence, notice subtle failure modes, and question results that seem too simple or too good to be true.
- Excellent written and verbal communication with researchers, engineers, customers, and non-specialists.
- Comfort with ambiguity, fast iteration, changing priorities, and occasional forward-deployed work with partner teams.
Responsibilities
- Design and run experiments to understand model behavior, test new ideas, and determine whether a dataset or training intervention actually works.
- Use fine-tuning and post-training experiments when useful to validate research hypotheses and measure improvements in the capabilities we care about.
- Create datasets, benchmarks, agent environments, rubrics, and evaluation methods for problems that are not well measured today.
- Work directly with AI labs and enterprise partners to turn open-ended model-development goals into concrete research and data projects.
- Dig into model outputs and trajectories to understand systematic failure modes and separate model limitations from problems in the data, grader, or evaluation setup.
- Write analysis code and build lightweight tools or prototypes for your own work, with support from engineers when a workflow needs to become reliable infrastructure.
- Share what you learn through clear technical reports, internal discussions, benchmarks, and research papers.
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