We are seeking an Engineer Associate with strong mechanical engineering and mechatronic design skills to develop next generation neurophysiology and intracortical brain-interfacing technology. The ideal candidate will play a critical role in prototyping lightweight, highly scalable, precisely controlled mechanical systems, e.g., precise linear actuation of Neuropixels probes, enabling the team to record neural activity and understand the brain at unprecedented scale. This role is ideal for individuals who thrive at the rapidly evolving interface of neuroscience, engineering, and software development, and want to contribute to a uniquely interdisciplinary effort. This position is embedded within a vibrant team of neuroscientists, engineers, and software developers led by Andreas Tolias (toliaslab.org), Tirin Moore (moorelabstanford.com), and collaborators.
    
    Role & Responsibilities:
    - Develop scalable and modular systems for neural recordings and animal behavior experiments
    - Design, prototype, and fabricate electro-mechanical components used in experimental neuroscience, including 3D-printed and machined parts
    - Design or integrate new approaches to miniaturized linear actuation and control, e.g., via piezoelectric motors, and online monitoring systems, e.g. via embedded cell-phone cameras and real-time, closed-loop computer vision software.
    - Maintain design libraries and documentation for versioning, reproducibility, and team collaboration
    - Collaborate closely with systems engineers and researchers to find creative solutions to evolving experimental needs

Key Qualifications:
    - Bachelor’s or Master’s degree in Mechanical Engineering, Biomedical Engineering, or a related field
    - Strong experience with CAD tools (e.g., Fusion, OnShape, SolidWorks)
    - Familiarity with software development, especially involving microcontrollers, control systems, prototype GUIs
    - Experience with rapid prototyping using 3D printers
    - Practical understanding of fabrication tolerances, material selection, and mechanical fabrication
    - Ability to work independently in a fast-paced, interdisciplinary environment and an interest in advancing the frontier of Neuro-AI

Preferred Qualifications:
    - Familiarity with simple electronic circuits, motor control, embedded cameras, and LEDs
    - Experience with programmatic CAD design, computational geometry, and/or CAD simulation
    - Interest in haptic interface robotics, related control systems, and VR environment simulation
    - Experience with developing miniature linear actuators (neurophysiology microdrives) or other lightweight, miniaturized experimental hardware
    - Hands-on experience in a machine shop or prototyping lab
    - Ability to work flexibly and collaboratively across multiple concurrent projects

What We Offer:
    - Work in a highly interdisciplinary environment bridging neuroscience, engineering, and AI
    - Access to in-house 3d printing facilities and Stanford’s world-class fabrication facilities
    - Opportunity to see your designs used in cutting-edge neuroscience experiments
    - Competitive salary and benefits
    - Mentorship and professional development


Application:
Please send your CV and one-page interest statement to: recruiting@enigmaproject.ai

We are seeking talented postdoctoral researchers with an extensive background in experimental systems neuroscience and excellent quantitative skills. Ideal candidates will have several years of practical experience performing neuro-behavioral and/or neuro-physiological experiments, including visual stimulus design, eye tracking, MRI or large-scale electrophysiology techniques (ideally Neuropixels). Additionally, candidates should possess a strong background in quantitative fields such as Mathematics, Physics, Engineering, or Computer Science. This is a collaborative, cross-functional team, and project assignments will be tailored to match each postdoc’s strengths and growth goals. If you are passionate about building high-quality neuroscience experiments and data systems in a highly interdisciplinary environment, we encourage you to apply. You will work closely with teams led by Andreas Tolias (toliaslab.org), Tirin Moore (moorelabstanford.com), and other collaborators at Stanford
    
    Role & Responsibilities:
    - Design and optimize large-scale electrophysiological and behavioral experiments using next generation custom-built hardware and software platforms
    - Develop and implement end-to-end experimental paradigms, including behavioral training and tracking, multi-Neuropixels recordings, imaging- and function-based recording path registration, and data quality control pipelines.
    - Collaborate closely with other teams in the Enigma Project to ensure efficient, scalable, and high-quality data collection and processing, with opportunities to explore scientific questions at the interface of neuroscience and AI in collaboration with theory and modeling teams.

Key Qualifications:
    - PhD in Neuroscience, Bioengineering, Electric Engineering, Computer Science, Physics, or a related field
    - Strong quantitative and analytical skills
    - Experience in either experimental neuroscience (e.g., in vivo neurophysiological recordings, behavioral training) or computational data analysis (e.g., spike sorting, neural signal processing)
    - Excellent communication and collaborative skills
    - A strong sense of curiosity and initiative, and a desire to collaboratively reimagine and reinvent traditional systems neuroscience methodologies

Preferred Qualifications: 
    - Hands-on experience with Neuropixels or other large-scale electrophysiological recordings
    - Experience designing, prototyping, and/or optimizing innovative experimental systems
    - Strong background and extensive knowledge in visual neuroscience, including anatomy, physiology, and modeling of visual systems
    - Background in developing visual, motor, or cognitive behavioral tasks and training animals
    - Experience implementing and optimizing eye tracking, body tracking, and/or visual reality environments
    - Proficiency in Python and scientific computing libraries
    - Familiarity with spike sorting workflows (e.g., Kilosort, SpikeInterface) and neural data quality control
    - Experience with imaging data processing, anatomical or functional registration, and 3D planning/reconstruction for recording trajectories


What We Offer:
    - A collaborative, interdisciplinary research environment spanning neuroscience, artificial intelligence, and systems engineering
    - Opportunities to work with cutting-edge tools and contribute to high-impact neuroscience infrastructure
    - Flexibility in project focus and opportunities to lead or co-lead initiatives based on your expertise
    - Competitive salary and benefits
    - Strong mentoring and career development support

Application:
Please send your CV and a one-page statement of interest to: recruiting@enigmaproject.ai

We are seeking a talented Software Developer to support and expand the infrastructure for large-scale behavioral and neural recordings. The ideal candidate will work closely with systems engineers and neuroscientists to develop the next generation of low-latency experimental control software and 3d neurophysiology planning tools. The Enigma project and the infrastructure we are developing spans many areas of expertise; we seek a highly motivated and creative individual who can learn new technology stacks and approaches. This role is ideal for individuals who thrive at the rapidly evolving interface of neuroscience, engineering, and software development, and want to contribute to a uniquely interdisciplinary effort. You will join a vibrant team within the laboratories of Andreas Tolias (toliaslab.org), Tirin Moore (moorelabstanford.com), and other Stanford labs known for their innovation in perception, behavior, large-scale neural recordings, and Neuro-AI.
    
    Role & Responsibilities:  
    - Collaborate with engineers and neuroscientists to design, build, and deploy next-generation experimental control systems tailored to high-throughput neural recording in rich, ethologically immersive behaviors
    - Help design and develop a 3d planning tool to optimize neurophysiology experimental design
    - Assist in scaling up our experimental systems and contribute to distributed data analysis pipeline
    - Contribute to version controlled, modular, robust, and well documented codebases
  
    Key Qualifications:
    - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
    - Proficiency in Python, version control (e.g., git), and collaborative software development workflows
    - Experience working with modular, distributed software and/or hardware/software integration
    - Familiarity with concepts such as state machines, event timing, inter-process and network communication
    - Ability to work independently in a fast-paced, interdisciplinary environment and an interest in advancing the frontier of Neuro-AI

    Preferred Qualifications:
    - Proficiency in performant, low-level languages, especially Rust or C/C++
    - Experience and/or interest in authoring software for 3D mesh geometry, rendering, collision detection and object packing optimization approaches
    - Experience and/or interest developing with modern columnar data systems, e.g. Polars, PyArrow, Parquet, Delta Lake, and distributed analysis pipeline technology stacks
    - Familiarity with hardware control libraries for data acquisition devices such as NI DAQ libraries and microcontrollers (e.g., Arduino, Teensy)
    - Interest in neuroscience or psychophysics (behavioral) experiments

    What We Offer:
    - A highly collaborative environment across neuroscience, AI, and systems engineering
    - Opportunity to contribute to a next-generation neurotechnology platform
    - Competitive salary and benefits
    - Strong mentoring and career development support

    Application:
    Please send your CV and one-page interest statement to: recruiting@enigmaproject.ai

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
    - Competitive salary and benefits
    - Strong mentoring in career development

    Application:
    Please send your CV and one-page interest statement to: recruiting@enigmaproject.ai

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 & 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