E11 Bio - Machine Learning Scientist I/II

Contribute to developing innovative ML approaches for automatic proofreading of segmentations in large PB-scale optical connectomics volumes, leveraging shape and barcode information

Own the deployment of scalable workflows to ingest large connectomic datasets into standard proofreading software (WebKnossos, CAVE)

Collaborate with academic and industry experts on building and extending tools for automated proofreading and interactive visualization

Develop, implement, and evaluate scalable pipelines for synapse detection and partner assignment in large-scale optical connectomics datasets, integrating morphological and protein labeling information

Identify and build ML-based image processing tools that remove key bottlenecks in our data processing and analysis pipeline

Maintain excellent laboratory science practices, communicate results through reports and presentations, and contribute to and assist in preparation of manuscripts, abstracts and other scientific documents.

Ph.D. in computer science, machine learning, applied math, computational biology or a related field in science and/or engineering or Ph.D.-equivalent publication history

3-5 years of relevant work or research experience, with strong history of independent and autonomous execution of novel ML solutions

Strong background in computer vision and applying ML tools to the analysis of large microscopy data sets

Experience with software engineering best practices (e.g. version control, code reviews, etc)

Excellent communication and collaboration skills, record keeping ability, and attention to detail

Experience with graph, sequence, or point based ML methods (GNNs, Transformers, Pointnets, etc.)

Experience with processing large chunked data (e.g Zarr) in the cloud (e.g. AWS or GCP)

Experience with large scale connectomics data and tools (e.g. Neuroglancer, WebKnossos, CAVE, CATMAID, etc.)

Strong record of publications in peer reviewed scientific journals a plus.

Become familiar with our existing ML and distributed processing code bases

Own the processing (segmentations) of a new dataset end-to-end and identify areas for improvement (accuracy and efficiency)

Integrate internal graph-based methods for automatic proofreading into standard block-wise workflows for PB-scale optical connectomics volumes  

Optimize ingestion of smaller segmentation graphs into standard proofreading software (e.g WebKnossos, CAVE) for improving efficiency of ground truth generation

Develop novel innovative ML approaches for automatic proofreading of neuron segmentations, leveraging both shape and barcode information

Contribute to algorithmic development across the segmentation pipelines increasing computational throughput

Contribute to achieving cubic-mm scale reconstruction at 100x lower proofreading costs

Scale ingestion of PB-sized optical volumes into proofreading software and pilot proofreading and dissemination of large connectomic subgraphs

You are passionate about moonshot neuroscience, and want to broadly accelerate science through long-term commitment to a high-impact project with a tight-knit team

You have a strong desire and intrinsic motivation to work collaboratively and inclusively as part of an interdisciplinary team science effort

You are committed to the ethos of open science, and want to see your work transparently disseminated

You’re mission-driven, and are seeking an environment where you can take long-term risks on moonshot science, shielded from academic or for-profit incentives

You’re seeking a high-leverage, non-traditional opportunity to maximize the impact of your science, establishing relationships with key stakeholders in the neuroscience community

An opportunity to help change the world and work with a world-class interdisciplinary team of scientists and engineers on a field-defining moonshot

Excellent medical, dental, and vision insurance through a PPO plan; parental leave.

Generous time off + paid holidays.

401K matching up to 6% of base salary

Ample healthy and energizing food including daily lunches.

Stipend for fitness and wellness activities.