Location: New Haven, CT

Organization: Bexorg Inc.

Reports to: Chief Technology Officer

About the Role

Bexorg Inc. is a translational neuroscience company developing a platform that preserves the cellular and molecular integrity of donated human brain tissue to accelerate therapeutic discovery for diseases such as Alzheimer disease and Parkinson disease. We are launching an initiative to capture the 3D structural, molecular, and omics profiles of these tissues at scale using a high-throughput robotics platform for human brain slices.

The Director of Bioengineering will be a visionary and hands-on leader responsible for building and executing a High-Throughput Brain Slice Platform: a massive fan-out drug screening program that generates up to 1,000 viable brain slices per perfused brain. These slices are incubated for up to 1 month, maintained by custom automated fluidics, and analyzed via imaging phenomics and multiscale omics to evaluate therapeutic perturbations.

Crucially, this role demands an engineering mindset obsessed with continuous iteration.

The baseline viability and fidelity of these brain slices are just the starting point. You will be expected to relentlessly optimize the platform, designing tight feedback loops that progressively enhance slice quality, longevity, and biological fidelity, ultimately maximizing their informational value for downstream therapeutic discovery.

Key Responsibilities

System Design, Execution & Relentless Iteration

• Scalable Architecture: Lead the engineering and operationalization of a highly scalable incubator platform capable of supporting up to 1,000 viable 300-micron by 8x8mm human brain slices per whole-brain run.

• Iterative Quality Enhancement: Design and enforce rigorous feedback loops between end-point omics/imaging data and upstream slice preparation/incubation. Use this data to continuously refine variables (e.g., media composition, flow rates, slice thickness) to relentlessly push the boundaries of tissue health and data yield.

• Automated Fluidics & Dosing: Oversee the deployment of continuous, robotically tracked aspirate-dispense fluid operations, ensuring each slice is managed with a precise, custom drug-dosing protocol over a 1-month survival window.

Phenomics, Omics & Pipeline Management

• Phenomics & Omics Integration: Develop protocols to bridge living slice assays with end-point imaging phenomics and high-throughput omics (e.g., snRNA-seq) to determine the exact cellular and molecular effects of dosed drug molecules.

• Adaptive Pipeline Management: Direct the automated pipeline from slicing and robotic mesh-cradle handling through rapid structural/molecular clearing and imaging protocols. Implement agile Quality Control (QC) gates that treat every batch as an opportunity to iterate and upgrade the baseline quality of the process.

• Advanced Microscopy: Oversee the operation of Light Sheet Microscope clusters, leveraging a combination of autofluorescence structure scans with active antibody staining.

• Targeted Omics & Expansion Retrieval: Develop automated CNC micro-dissection/laser capture (LCM) workflows for spatial transcriptomics/proteomics (Orbitrap/NextSeq) and nanoscale MultiExR (Multiplexed Expansion Retrieval) processing.

Computational Leadership & AI Integration

• Data Infrastructure: Oversee the capture, streaming, and storage of petabyte-scale data over a 100GbE network fabric.

• Virtual Staining & Registration: Collaborate with the computational biology and AI teams in training and deploying 'Virtual Proteome' AI models that seamlessly register multi-modal informatics and imaging streams into unified representations used for scientific research.

Strategic & Team Leadership

• Culture of Continuous Improvement: Build and mentor a world-class, multi-disciplinary team of imaging specialists, lab operations managers, robotics engineers, data engineers, and computational biologists. Instill a team culture where sub-optimal yields are rapidly analyzed and transformed into actionable, iterative protocol upgrades.

• Budget & Scale: Manage a multi-million dollar CapEx and OpEx budget, driving the facility's scale-up from an 'Alpha' proof-of-concept to an industrial bio-foundry processing 1,000 brains/year.

Ideal Candidate Profile & Qualifications

We are seeking an expert in biomedical image analysis with a strong neuroscience background and proven experience managing large high-throughput scientific projects with sophisticated hardware and software requirements. You must be an inspiring, resilient, and hands-on leader capable of guiding high-impact dynamic engineering and research teams supporting drug discovery, brain reconstruction, connectomics, and cell and tissue biophysics. Most importantly, you must possess a relentless drive foroptimization—you don't just build pipelines; you continuously stress-test and refine them to squeeze increasing value out of biological samples over time.

• Education: Ph.D. in Biophysics, Bioengineering, Computer Science, or a closely relatedfield.

• Domain Expertise: Deep understanding of Microscopy, modeling in the Life Sciences, and AI/ML applications to biomedical Computer Vision and Omics.

• Iterative Drive: A demonstrated obsession with process refinement and the ability to pivot engineering strategies based on emergent biological data to progressively improve tissue viability and assay signal-to-noise ratios.

• Wet-Lab & Microscopy Integration: Hands-on wet-lab and microscopy experience, with a proven ability to design and optimize novel post acquisition suites of tools for processing large image volumes (e.g., light-sheet microscope data).

• Computational Infrastructure: Deep understanding of scientific software engineering, ML ops, and AI infrastructure, including cloud and High-Performance Computing (HPC).

• Strategic Execution: Proven track record of high-impact publications and production pipeline development at top international institutions. Experience bridging fast-paced academic environments with biotech/startup execution.

• Leadership: Demonstrated history of unlocking funding, mapping strategic multi-year plans, and transitioning AI/Computational teams into dynamic, top-tier internal units while scaling up throughput, accuracy, and output quality.