Machine Learning Scientist

Overview

E11 Bio is a Focused Research Organization (FRO) with a long-term mission to make connectomics scalable and cost-effective for mammalian brains – first for mice and eventually for humans.

We have recently developed PRISM, a platform technology for low cost optical connectomics. PRISM integrates advances in genetic cell barcoding, multiplexed expansion microscopy, and machine learning (ML)-based spatial reconstruction to solve key bottlenecks in data acquisition and analysis. We anticipate PRISM will reduce the cost of connectomics 100-fold and thereby transform neuroscience, treatments for brain disorders, and neuro-inspired computing.

E11 Bio is seeking a creative and mission-driven Machine Learning Scientist to develop and deploy novel methods to transform large-scale light microscopy data into discoverable synaptic connectivity information. In this role, you will own core ML pipelines for synapse detection and assignment in optical connectomics datasets, working closely with wet lab scientists, microscopists, and domain experts to ensure biological fidelity and scalability.

Your work will directly support E11’s goal of making reliable connectomic data accessible to the broader neuroscience community.

• Develop, implement, and evaluate scalable pipelines for synapse detection and partner assignment in large-scale optical connectomics datasets, integrating morphological and protein labeling information
• Collaborate with wet lab scientists and domain experts to develop methods for detecting and classifying cellular and synaptic subtypes
• Take ownership of structuring and organizing connectivity data into well-defined data models, and building developer-facing tooling to maximize the long-term usability, accessibility, and impact of generated connectivity data
• Assist in any ML-adjacent image processing needs that may arise during scaling of the processing and analysis pipeline (e.g. image volume registration, multimodal feature extraction, label-free prediction etc.)
• Collaborate closely with users and stakeholders to deliver clear, accurate, and actionable connectivity information
• Partner with wet lab, microscopy, and computational teams to optimize data analysis workflows and inform data acquisition strategies

• Ph.D. in computer science, machine learning, applied math, computational biology or a related field in science and/or engineering or 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 large scale connectomics datasets, tools, and database infrastructure (e.g. Neuroglancer, CATMAID, CAVE)
• Familiarity with distributed processing frameworks for large-scale volumetric data (e.g. Dask, Daisy, Volara)
• Experience with Python-based ML libraries such as PyTorch, Tensor Flow, and Gunpowder
• Experience developing database APIs and data services (e.g. Postgre
  
  SQL, Neo4j, Big Table), as well as user-facing data visualization tools (e.g. Dash, Plotly, D3)

• Developed and deployed a robust pipeline for the routine detection of excitatory and inhibitory synaptic contacts between reconstructed neurons
• Supported the team in diverse image processing challenges encountered in the process of scaling our pipeline

• Delivering accurate, discoverable, and actionable connectivity data to users through close collaboration with internal and external stakeholders
• Expanding the impact and capabilities of the PRISM platform by integrating information on cell types, synaptic properties, and other cellular and subcellular features

• You are passionate about moonshot neuroscience, and want to broadly accelerate science through long-term…