Founding Scientist

Parasma, San Francisco, California +1

Nice to have

Well published in information theory or related fields
Deep understanding of learning systems, optimization, exploration, credit assignment, and adaptive control
Experience designing experiments for complex biological, neural, or adaptive systems
Understanding of the challenges involved in training biological substrates
Currently or previously a professor, PI, or senior research scientist at a top institution
Proven ability to lead an independent research agenda

Scientific roadmap for training biological substrates
Design, execute, and analyze closed-loop training experiments
Develop learning protocols for biological neural systems
Define reward structures, stimulation policies, feedback loops, and evaluation metrics
Build theoretical frameworks and benchmarks around learning, control, and information transfer in biological substrates
Identify and solve core bottlenecks in biological training, including noise, variability, non-stationarity, and limited observability
Work with engineering to build the software, hardware, and ML infrastructure required for real-time training

P
Machine Learning Scientist, Pretraining
ProfluentEmeryville, California +2
- Hybrid
- Inference & ML systems
$212K–$339K / yr
Posted Aug 3, 2026
Founding Research Scientist, Robot Learning
GRAMSan Francisco Bay Area, California +1

Nice to have

Well published in information theory or related fields
Deep understanding of learning systems, optimization, exploration, credit assignment, and adaptive control
Experience designing experiments for complex biological, neural, or adaptive systems
Understanding of the challenges involved in training biological substrates
Currently or previously a professor, PI, or senior research scientist at a top institution
Proven ability to lead an independent research agenda

Parasma is looking for a founding scientist to own the following

Scientific roadmap for training biological substrates
Design, execute, and analyze closed-loop training experiments
Develop learning protocols for biological neural systems
Define reward structures, stimulation policies, feedback loops, and evaluation metrics
Build theoretical frameworks and benchmarks around learning, control, and information transfer in biological substrates
Identify and solve core bottlenecks in biological training, including noise, variability, non-stationarity, and limited observability
Work with engineering to build the software, hardware, and ML infrastructure required for real-time training