Profluent
Machine Learning Scientist, Reinforcement Learning
About this role
Profluent is seeking a Machine Learning Scientist specializing in reinforcement learning to enhance biomolecular design through generative modeling. The role involves collaborative research, algorithm development, and core infrastructure optimization in a fast-paced, interdisciplinary environment.
What you'll do
- Develop online and offline reinforcement learning algorithms for protein design
- Collaborate with teams to adapt reinforcement learning techniques for biomolecular applications
- Implement and optimize core infrastructure for protein language models
- Curate datasets and design evaluation tasks for generative models
- Analyze and present computational approach results to colleagues
- Shape scientific and strategic vision in a collaborative team setting
What they're looking for
- PhD in relevant field or equivalent experience
- Experience with innovative machine learning techniques
- Publications at major ML or scientific conferences/journals
- Proficiency in deep learning frameworks like Pytorch or Jax
- Familiarity with cloud computing platforms (GCP, AWS, etc.)
- Data extraction and curation experience in bioinformatics
- Knowledge of protein biology (preferred)
- Experience with wet lab assays (preferred)
Benefits
- High-growth opportunity with significant impact
- Competitive salary with equity options
- 401(k) with employer match
- Comprehensive health, dental, and vision insurance
- Generous paid time off policy
- Professional development in AI and biology
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Profluent
Profluent develops AI-powered protein design and biomolecular engineering solutions, leveraging machine learning and generative modeling to advance biomedical applications. The company is hiring Machine Learning Scientists specializing in reinforcement learning and generative models, as well as Sustaining Engineers to maintain and enhance their automated high-throughput operations infrastructure.
View all jobs at ProfluentLikely interview questions
- Walk us through a reinforcement learning project you've published or worked on. How did you approach designing the reward function and handling exploration-exploitation tradeoffs?
- Describe your experience implementing RL algorithms (online or offline) in PyTorch or JAX. What challenges did you encounter and how did you optimize for training efficiency?