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Job description
About the company
Our client is an artificial intelligence company.
The role
Raydar is recruiting for this role on behalf of our client. Carry out hands-on research on publicly available neural networks, testing methods for the early and refinement stages of learning, and build the measurement tools that confirm whether a change genuinely helps. The role takes a research question from first idea through to a working implementation, working closely with engineers who build production systems, and calls for a high degree of independence.
What you'll do
- Plan and carry out studies on publicly available neural networks, choosing the learning method that suits each question.
- Build systems that transform records of human work into material suitable for learning.
- Explore ways to represent workflows, context and decisions so that model performance improves.
- Create measures of output quality and dependability across lengthy, multi-step tasks.
- Set up repeatable experiment and training routines.
- Work alongside infrastructure engineers to move proven methods into live systems.
- Take a research question from first idea to a working result, owning each step.
- Investigate failed training runs and tell real gains apart from results that only look promising.
Requirements
What we're looking for
- 2 to 5 years of relevant experience.
- Practical, hands-on time with publicly available neural networks, including their foundational or refinement training phases.
- Coding proficiency proven by building research prototypes, learning infrastructure or repeatable test setups.
- Track record standing up data preparation flows for learning systems and gauging trained model quality.
- Experience designing studies independently and working out why training runs fail.
- Sound grasp of how data quality, parameter tuning, measurement and study design interact.
- Ability to turn research findings into measurable gains on extended tasks.
- Comfort working on-site five days per week.
Bonus points
- Prior work at an organization centered on applied AI is a plus.
- Work with models that learn from records of how people carry out tasks.
- Familiarity with making learning runs faster and cheaper, or with spreading computation across many machines.
- Shared research outputs or public code that others can use.
- Startup or entrepreneurial experience.
Benefits
Compensation and benefits
- Equity
- Health, dental and vision coverage
Location and work model
- New York, NY, United States
- On-site, five days per week in office
- Full-time