Open Positions
Join us. Our team at Stanford has pioneered methods in NeuroAI, neural decoding, interpretability, neuroscience theory, and neural recording technology.
-
We are seeking exceptional research scientists to pioneer the development of foundation models that bridge artificial and biological intelligence. You will lead the development of large-scale transformer-based architectures that integrate diverse neural data streams—from visual stimuli to high-dimensional neuronal recordings and behavioral measurements. This position offers a unique opportunity to push the boundaries of self-supervised learning and multi-task objectives, creating models that not only predict neural responses but reveal fundamental principles of biological computation. The ideal candidate will have extensive experience developing multimodal foundation models and interest in pioneering the application of these techniques for decoding the neural basis of intelligence.
Role & Responsibilities:
Design novel transformer-based architectures for integrating continuous visual, neural, and behavioral time series data
Develop self-supervised learning approaches and multi-task objectives for training foundation models of the brain
Pioneer new methods for modeling the relationship between sensory inputs and neural activity across the visual hierarchy
Lead research in scaling model architectures to process and integrate massive neurophysiological datasets
Guide technical strategy for model evaluation, validation, and interpretation
Advance the field through publications and presentations at top machine learning and computational neuroscience venues
Key qualifications:
Ph.D. in Computer Science, Machine Learning, Computational Neuroscience, or a related field, plus 2+ years post-Ph.D. research experience
At least 2+ years of practical experience in training, fine-tuning, and using multi-modal deep learning models
Strong publication record in top-tier machine learning conferences and journals, particularly in areas related to multi-modal modeling
Strong programming skills in Python and deep learning frameworks
Demonstrated ability to lead research projects and mentor others
Ability to work effectively in a collaborative, multidisciplinary environment
Preferred Qualifications:
Background in theoretical neuroscience or computational neuroscience
Experience in processing and analyzing large-scale, high-dimensional data of different sources
Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services
Familiarity with big data and MLOps platforms (e.g. MLflow, Weights & Biases)
Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)
Experience with large-scale distributed model training frameworks (e.g. Ray, DeepSpeed, HF Accelerate, FSDP)
What we offer:
A rich environment in which to pursue fundamental research questions in AI and neuroscience
A dynamic team of engineers and scientists in a project dedicated to one mission, rooted in academia but inspired by science in industry.
Access to unique datasets spanning artificial and biological neural networks
State-of-the-art computing infrastructure
Competitive salary and benefits package
Collaborative environment at the intersection of multiple disciplines
Location at Stanford University with access to its world-class research community
Strong mentoring in career development.
Application:
Please send your CV and one page interest statement to: recruiting@enigmaproject.ai
-
We are seeking exceptional engineers to build and scale the next generation of brain foundation models. You will develop robust infrastructure for training large-scale transformer architectures that process continuous, multi-dimensional neural and behavioral time series data. This role focuses on implementing efficient training pipelines, optimizing model architectures, and solving the unique engineering challenges of working with massive neurophysiological datasets. The ideal candidate will have extensive experience implementing and scaling multimodal foundation models and a drive to tackle the computational challenges of modeling biological intelligence. This position offers an opportunity to build the technical foundation for a new understanding of how the brain processes information.
Role & Responsibilities:
Implement and optimize the latest machine learning algorithms/models to train multimodal foundation models on neural data
Develop and maintain scalable, efficient, and reproducible machine-learning pipelines
Conduct large-scale ML experiments, using the latest MLOps platforms
Run large-scale distributed model training on high-performance computing clusters or cloud platforms
Collaborate with machine learning researchers, data scientists, and systems engineers to ensure seamless integration of models and infrastructure
Monitor and optimize model performance, resource utilization, and cost-effectiveness
Stay up-to-date with the latest advancements in machine learning tools, frameworks, and methodologies
Key qualifications:
Master's or Ph.D. in Computer Science, Machine Learning, or a related field
2-3 years of practical experience in implementing and optimizing machine learning algorithms with distributed training using common libraries (e.g. Ray, DeepSpeed, HF Accelerate, FSDP)
Strong programming skills in Python, with expertise in machine learning frameworks like TensorFlow or PyTorch
Experience with orchestration platforms
Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services
Familiarity with MLOps platforms (e.g. MLflow, Weights & Biases)
Strong understanding of software engineering best practices, including version control, testing, and documentation
Preferred qualifications:
Familiarity with training, fine tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)
Familiarity with modern big data tools and pipelines such as Apache Spark, Arrow, Airflow, Delta Lake, or similar
Experience with AutoML and Neural Architecture Search (NAS) techniques
Contributions to open-source machine learning projects or libraries
What we offer:
Work on a collaborative and uniquely positioned project spanning several disciplines, from neuroscience to artificial intelligence and engineering.
Work jointly with a vibrant team of researchers and scientists in a project dedicated to one mission, rooted in academia but inspired by science in industry.
Competitive salary and benefits.
Strong mentoring in career development.
Application:
Please send your CV and one page interest statement to: recruiting@enigmaproject.ai
See full details here.
-
We are seeking exceptional research scientists who can advance the theoretical foundations of interpretability research while developing novel methods for understanding computational principles in both artificial and biological neural networks. This position will drive forward our understanding of how large-scale neural systems process and represent information, with the unique opportunity to apply and develop interpretability techniques across both artificial and biological systems. The role combines cutting-edge research in mechanistic interpretability with the opportunity to impact our understanding of both artificial and biological intelligence.
Role & Responsibilities:
Lead development of automated methods for interpreting large-scale neural networks and biological data
Design algorithms for discovering computational principles and circuits in neural systems
Advance techniques for feature visualization, geometric analysis, and manifold learning in high-dimensional neural data
Develop causal intervention methods to map information flow in neural networks
Create tools for automated hypothesis generation and testing in neural systems
Collaborate with neuroscientists to validate interpretability findings in biological systems
Guide technical strategy for scaling interpretability methods to massive datasets
Key Qualifications:
Ph.D. in Computer Science, Mathematics, Neuroscience, or related field plus 2+ years post-Ph.D. research experience
Strong publication record in machine learning, particularly in areas related to model interpretability
Deep understanding of mechanistic interpretability literature and methods
Expertise in analyzing and interpreting deep neural networks
Experience with automated scientific discovery systems or agentic AI
Strong programming skills with experience in modern ML frameworks
Demonstrated ability to lead research projects and mentor others
Excellent written and verbal communication skills
Preferred Qualifications:
Experience developing novel interpretability methods
Background in theoretical neuroscience or computational neuroscience
Knowledge of differential geometry and its applications to neural representations
Familiarity with large-scale machine learning systems
Track record of open-source contributions to interpretability tools
Experience with large language models or multimodal architectures
History of successful research collaborations across disciplines
Research Areas of Interest:
Novel methods for mechanistic interpretability at scale
Geometric approaches to understanding neural representations
Development of AI scientists for automated hypothesis generation and testing
Techniques for discovering and validating computational circuits
Comparative analyses between artificial and biological neural networks
Causal intervention methods for understanding network computation
Mathematical frameworks for neural information processing
What We Offer:
An environment in which to pursue fundamental research questions in AI and neuroscience interpretability
Access to unique datasets spanning artificial and biological neural networks
State-of-the-art computing infrastructure
Competitive salary and benefits package
Collaborative environment at the intersection of multiple disciplines
Location at Stanford University with access to its world-class research community
Application:
Please send your CV and a one-page statement of interest to: recruiting@enigmaproject.ai
-
We are seeking exceptional engineers to develop and deploy scalable pipelines for analyzing and interpreting foundation models of the brain, helping us understand how the brain represents and processes information. This position will focus on applying and scaling state-of-the-art neural analyses and interpretability techniques to uncover meaningful structures and circuits within our brain foundation models. The role combines rigorous engineering practices with cutting-edge research in model interpretability, working at the intersection of neuroscience and artificial intelligence.
Role & Responsibilities:
Design and implement scalable pipelines for automated interpretability analyses of brain foundation models
Develop infrastructure for running massive-scale in silico experiments on digital twins
Build tools for automated circuit discovery and geometric/topological analysis of neural manifolds
Create efficient, reproducible analysis workflows for processing high-dimensional neural data
Engineer systems for automated hypothesis generation and testing
Implement and scale feature visualization and manifold learning techniques
Maintain distributed computing infrastructure for parallel interpretability analyses
Develop interactive visualization tools for exploring neural representations
Key Qualifications:
Master's degree in Computer Science or related field with 2+ years of relevant industry experience, OR Bachelor's degree with 4+ years of relevant industry experience
Strong understanding of mechanistic interpretability techniques and research literature
Expertise in implementing and scaling ML analysis pipelines
Experience with high-performance computing and distributed systems
Proficiency in Python and deep learning frameworks (i.e. PyTorch)
Experience with distributed computing and high-performance computing clusters
Strong software engineering practices including version control, testing, and documentation
Familiarity with visualization tools and techniques for high-dimensional data
Preferred Qualifications:
Experience with feature visualization techniques (e.g., activation maximization, attribution methods)
Knowledge of geometric methods for analyzing neural population activity
Familiarity with circuit discovery techniques in neural networks
Experience with large-scale data processing frameworks
Background in neuroscience or computational neuroscience
Contributions to open-source ML or interpretability tools
Experience with ML experiment tracking platforms (W&B, MLflow)
What We Offer:
Opportunity to work on fundamental questions in AI interpretability and neuroscience
Collaborative environment bridging academic research and engineering excellence
Access to state-of-the-art computing resources and unique neural datasets
Competitive salary and benefits
Career development and mentoring
Location at Stanford University with access to its vibrant research community
Application:
Please send your CV and a one-page statement of interest to: recruiting@enigmaproject.ai
See full details here.
For all hiring inquiries: recruiting@enigmaproject.ai.