Position Overview

We are looking for a Bioinformatics Scientist to join our Computational Team to help build the analytical foundation that connects nanopore measurements to biological understanding. This role sits at the intersection of biology, physics, signal processing, and machine learning - you will work directly with experimental scientists and engineers to develop methods that transform complex biological measurements into actionable scientific insight.

This role requires creating and implementing standard pipeline, and goes beyond them. Here, you will build fundamental approaches to understanding and processing electronic data into an interpretable format and create methods to transform that data into actionable sequencing outputs. The problems are open-ended, and many of the analytical frameworks don't exist yet. The right person enjoys working with noisy experimental data, building quantitative models from first principles, and collaborating across disciplines.

What You Will Do

• Analyze nanopore and electrophysiology datasets to identify biologically meaningful signal features.
• Develop and implement computational workflows for event detection, classification, and molecular fingerprinting.
• Build quantitative models that connect nanopore measurements to molecular identity, structure, and function.
• Collaborate with experimental scientists to interpret results and shape the direction of future experiments.
• Partner with protein science, signal processing, and machine learning teams to improve platform performance and analytical capabilities.
• Help establish best practices for benchmarking, validation, and uncertainty quantification as the platform scales.

Qualifications

Required

• Master's degree plus 3+ years of relevant industry experience, or PhD in Computational Biology, Biophysics, Bioengineering, Physics, Applied Mathematics, Computer Science, Electrical Engineering, or a related quantitative discipline.
• Strong programming skills in Python and scientific computing environments.
• Experience analyzing complex biological, physical, or time-series datasets - particularly in the presence of noise and experimental variability.
• Solid background in at least two of: statistics, machine learning, signal processing, or Bayesian modeling.
• Comfortable working across experimental and computational domains; able to engage meaningfully with wet-lab scientists on data interpretation.
• Clear scientific communicator - written and verbal.

Preferred

• Hands-on experience with nanopore technologies, electrophysiology, or single-molecule measurement platforms.
• Experience applying deep learning or probabilistic modeling to biological or physical datasets.
• Background in computational biophysics or time-series analysis of high-dimensional biological signals.

The salary range for this role is $115,000 - $165,000. Compensation for the role will depend on a number of factors, including a candidate's qualifications, skills, competencies, and experience. FL110 currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on FL110's good faith estimate as of the date of publication and may be modified in the future.