# Machine Learning Research Engineer > Raydar · New York, United States · Full-time · Posted 2026-11-14 **Workplace:** on_site ## 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 ## Apply [Apply at Raydar](https://apply.workable.com/raydar/j/513FBA0DB3/apply) --- Powered by [Workable](https://www.workable.com)