Machine Learning Research Engineer

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