E11 Bio - Machine Learning Scientist I/II

Responsibilities • 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.

Required qualifications • 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

Preferred qualifications • 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.

In the first six months, you will: • 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

In 12 months, you will: • 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 would love this job if… • 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

We Offer • 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.