Stanford University is launching an interdisciplinary Neuro-AI project dedicated to building a foundation model of the brain. This endeavor will involve multiple labs and faculty across the Stanford campus, including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute. Leveraging cutting-edge advances in electrophysiology and machine learning, this project aims to create a functional "digital twin" -- a model that captures both the activity dynamics of the brain at cellular resolution and the intelligent behavior it generates.
We are seeking a highly skilled Computer Vision Engineer (Software Developer 2) to join our interdisciplinary team. This role will be pivotal in bridging the gap between neuroscience and artificial intelligence by developing and implementing cutting-edge computer vision techniques to analyze and annotate complex visual datasets. The ideal candidate will have a strong background in computer vision, deep learning, and mathematical modeling, with a keen interest in applying these skills to advance our understanding of neural processing in the visual cortex.
This position promises a vibrant and cooperative atmosphere within the laboratories of Andreas Tolias (), Tirin Moore () and other labs at Stanford University renowned for their expertise in perception, cognition, pioneering neural recording techniques, computational neuroscience, machine learning, and Neuro-AI research.
Role & Responsibilities:
* Design and implement robust machine learning pipelines for processing and analyzing large-scale video datasets
* Curate and refine datasets of natural videos for use as stimuli in cutting-edge neuroscience experiments
* Implement and adapt state-of-the-art computer vision algorithms to generate comprehensive annotations and metadata for video datasets, including object segmentation, categorization, optic flow, depth estimation, and semantic scene understanding
* Develop and apply mathematical methods to quantify and optimize the coverage of natural scene statistics/manifolds within curated datasets
* Collaborate closely with the modeling team to integrate featurized datasets into a large-scale multi-modal foundation model of the visual cortex
* Contribute to the development of novel algorithms that bridge computer vision techniques with neuroscientific insights
DESIRED QUALIFICATIONS:
* PhD in Computer Vision, Machine Learning, or a closely related field, or equivalent practical experience
* Postdoctoral experience or industry experience in applying computer vision techniques to complex, real-world problems
* Experience with large-scale data processing and analysis using cloud computing platforms (e.g., AWS, Google Cloud)
* Experience with multi-modal learning and integration of visual and non-visual data
* Track record of publications in top-tier computer vision or machine learning conferences (e.g., CVPR, ICCV, NeurIPS, ICML, ICLR)
* Experience with geometric foundations relevant to computer vision and representation learning
* Familiarity with neuroscience concepts, particularly in visual perception and cortical processing
* Demonstrated ability to work effectively in interdisciplinary teams
* Demonstrated expertise in Python, PyTorch, and other deep learning frameworks (e.g., TensorFlow, JAX)
* Proven track record of implementing and adapting state-of-the-art computer vision algorithms for large-scale image or video analysis
* Strong programming skills and experience in building scalable, efficient ML pipelines
* Experience with version control systems (e.g., Git) and software development best practices
* Extensive knowledge of modern computer vision techniques, including but not limited to object detection, segmentation, tracking, and scene understanding
* Solid mathematical foundations, particularly in areas relevant to computer vision and machine learning
* Excellent problem-solving skills and ability to think creatively about dataset curation and representation learning
*
EDUCATION & EXPERIENCE (REQUIRED):
* Bachelor's degree and five years of relevant experience, or a combination of education and relevant experience.
KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):
* Expertise in designing, developing, testing, and deploying applications.
* Proficiency with application design and data modeling.
* Ability to define and solve logical problems for highly technical applications.
* Effective communication skills with both technical and non-technical clients.
* Ability to lead activities on structured team development projects.
* Ability to select, adapt, and effectively use a variety of programming methods.
* Knowledge of application domain.
CERTIFICATIONS & LICENSES:
None
PHYSICAL REQUIREMENTS*:
* Constantly perform desk-based computer tasks.
* Frequently sit, grasp lightly/fine manipulation.
* Occasionally stand/walk, writing by hand.
* Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds.
* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.
WORKING CONDITIONS:
* May work extended hours, evening and weekends.
* Travel on campus to school/units
The expected pay range for this position is $131,992 to $160,555 per annum. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.
At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.
In addition to applying on this site, please send your CV and one page interest statement to: [email protected]
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