Autoscience
Machine Learning Research Scientist
About this role
Autoscience Institute seeks a Machine Learning Research Scientist to develop autonomous AI systems that conduct research independently. You'll work closely with the founder and engineering team to build production-ready systems that ideate, experiment, and improve models using reinforcement learning and advanced training techniques.
What you'll do
- Develop autonomous research systems that ideate, experiment, and improve customer models
- Collaborate with engineering team to build and deploy production-ready research systems
- Apply RL post-training and fine-tuning to reasoning models for automating ML research processes
- Stay current with latest AI research and automation developments
- Work directly with founder on cutting-edge autonomous research initiatives
What they're looking for
- Deep learning and model training
- Reinforcement learning
- Machine learning research and publication
- Scalable ML pipelines and distributed training
- Large-scale model training (64+ GPUs)
- Automated scientific research
- Production ML systems deployment
- AI research methodology
Benefits
- Competitive compensation matching major LLM frontier labs
- Unlimited PTO and flexible working arrangements
- Conference attendance and publication support
- Opportunity to advance autonomous scientific discovery
- Work with passionate research team on cutting-edge problems
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Autoscience
Autoscience builds autonomous AI systems designed to conduct independent research and accelerate scientific discovery through reinforcement learning and advanced model training. The company is hiring Machine Learning Research Scientists and Software Engineers to develop and maintain production-ready backend infrastructure supporting their AI research platform.
View all jobs at AutoscienceLikely interview questions
- Walk us through a top-tier conference paper you've published or contributed to significantly—what was your specific role and what made the research novel?
- Describe your experience with reinforcement learning or genetic algorithms in a production or research setting. What challenges did you encounter and how did you overcome them?