Allen Institute for Cell Science

We seek a motivated, knowledgeable, and team-oriented scientist with excellent machine learning knowledge, interested in applying these skills to biology as part of the Computational Cell Science team in the Allen Institute for Cell Science.. Develop and implement scalable and reproducible machine learning pipelines for 3D shape quantification on data from microscopy image-based assays. Ph. D. in Computational Physics, Applied Physics, Applied Mathematics, Computer Science, Biological Science (e.g. Cell Biology, Biophysics, Bioengineering), or related science or engineering field; OR equivalent combination of degree and experience. Extensive knowledge in machine learning and hands-on experience in developing and implementing deep learning algorithms and generative models like VAEs, GANs, autoregressive models and transformers. Experience with image-based biology assays; some experimental and/or microscopy image analysis experience would be an advantage

machine learning

The mission of the Allen Institute for Cell Science is to create multi-scale visual models of cell organization, dynamics, and activities. Our approach encompasses large-scale data collection, observation, theory, and predictions to understand cellular behavior in normal and pathological contexts. As a division within the Allen Institute, the Allen Institute for Cell Science uses a team-oriented approach, focusing on accelerating foundational research, developing standards and models, and cultivating new ideas to make a transformational impact on science. The goal of the Computational Cell Science team is to develop scalable, quantitative image-based analysis frameworks of cell organization, activity, and function. We seek a motivated, knowledgeable, and team-oriented scientist with excellent machine learning knowledge, interested in applying these skills to biology as part of the Computational Cell Science team in the Allen Institute for Cell Science. This Scientist position will develop machine learning workflows for generative shape modeling from single cell image data. The Allen Institute believes that team science significantly benefits from the participation of diverse voices, experiences, and backgrounds. High-quality science can only be produced when it includes different perspectives. We are committed to increasing diversity across every team and encourage people from all backgrounds to apply for this role. Essential Functions <ul><li> Develop and implement scalable and reproducible machine learning pipelines for 3D shape quantification on data from microscopy image-based assays </li><li> Systematically, efficiently and reproducibly iterate on new models </li><li> Maintain and improve existing machine learning pipelines </li><li> Work closely with other teams in the institute to scale-up analysis protocols into a high throughput computational pipeline </li><li> Ensure seamless integration and sharing of resources and data across teams </li><li> Maintain rigorous quality control standards </li><li> Maintain meticulous records and work closely with other scientists to coordinate complex experiments </li><li> Adherence to SOPs, GLPs and regulatory requirements </li><li> Prepare written summaries and present activities internally and publicly </li></ul>Note: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned. Required Education And Experience <ul><li> Ph.D. in Computational Physics, Applied Physics, Applied Mathematics, Computer Science, Biological Science (e.g. Cell Biology, Biophysics, Bioengineering), or related science or engineering field; OR equivalent combination of degree and experience </li></ul>Preferred Education And Experience <ul><li> Extensive knowledge in machine learning and hands-on experience in developing and implementing deep learning algorithms and generative models like VAEs, GANs, autoregressive models and transformers </li><li> Experience with image-based biology assays; some experimental and/or microscopy image analysis experience would be an advantage </li><li> Experience with different data representations like images, point clouds, meshes; some computational geometry experience would be an advantage </li><li> Experience with developing or contributing to open-source tools/packages </li><li> Experience utilizing software engineering practices such as version management, build management and testing; Experience with MLOps tools like MLflow, prefect would be an advantage </li><li> Careful attention to detail </li><li> Excellent interpersonal skills </li><li> Experience working in a multi-disciplinary environment in academic or industrial settings </li><li> Ability to work both independently and in a collaborative, multi-disciplinary environment </li></ul> Work Environment <ul><li> May enter laboratory environment, including potential exposure to lasers, biohazards </li></ul> Physical Demands <ul><li> Fine motor movements in fingers/hands to operate computers and other office equipment; repetitive motion with lab equipment </li></ul> Position Type/Expected Hours of Work <ul><li> This role is currently able to work in a hybrid work environment. We are a Washington State employer, and any remote work must be performed in Washington State. </li></ul> Annualized Salary Range <ul><li>$82,650 - $115,700 * </li><li> Final salary depends on the required education for the role, experience, level of skills relevant to the role, and work location, where applicable. </li></ul>Benefits Employees (and their families) are eligible to enroll in benefits per eligibility rules outline in the Allen Institute’s Benefits Guide. These benefits include medical, dental, vision, and basic life insurance. Employees are also eligible to enroll in the Allen Institute’s 401k plan. Paid time off is also available as outlined in the Allen Institutes Benefits Guide. Details on the Allen Institute’s benefits offering are located at the following link to the Benefits Guide: https://alleninstitute.org/careers/benefits . It is the policy of the Allen Institute to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Allen Institute will provide reasonable accommodations for qualified individuals with disabilities.

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